Endpoint-Based Parallel Data Processing In A Parallel Active Messaging Interface Of A Parallel Computer

ABSTRACT

Endpoint-based parallel data processing in a parallel active messaging interface (‘PAMI’) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes coupled for data communications through the PAMI, including establishing a data communications geometry, the geometry specifying, for tasks representing processes of execution of the parallel application, a set of endpoints that are used in collective operations of the PAMI including a plurality of endpoints for one of the tasks; receiving in endpoints of the geometry an instruction for a collective operation; and executing the instruction for a collective operation through the endpoints in dependence upon the geometry, including dividing data communications operations among the plurality of endpoints for one of the tasks.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Contract No.B544331 awarded by the Department of Energy. The Government has certainrights in this invention.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically,methods, apparatus, and products for endpoint-based parallel dataprocessing in a parallel active messaging interface (‘PAMI’) of aparallel computer.

2. Description of Related Art

The development of the EDVAC computer system of 1948 is often cited asthe beginning of the computer era. Since that time, computer systemshave evolved into extremely complicated devices. Today's computers aremuch more sophisticated than early systems such as the EDVAC. Computersystems typically include a combination of hardware and softwarecomponents, application programs, operating systems, processors, buses,memory, input/output devices, and so on. As advances in semiconductorprocessing and computer architecture push the performance of thecomputer higher and higher, more sophisticated computer software hasevolved to take advantage of the higher performance of the hardware,resulting in computer systems today that are much more powerful thanjust a few years ago.

Parallel computing is an area of computer technology that hasexperienced advances. Parallel computing is the simultaneous executionof the same application (split up and specially adapted) on multipleprocessors in order to obtain results faster. Parallel computing isbased on the fact that the process of solving a problem usually can bedivided into smaller jobs, which may be carried out simultaneously withsome coordination.

Parallel computers execute parallel algorithms. A parallel algorithm canbe split up to be executed a piece at a time on many differentprocessing devices, and then put back together again at the end to get adata processing result. Some algorithms are easy to divide up intopieces. Splitting up the job of checking all of the numbers from one toa hundred thousand to see which are primes could be done, for example,by assigning a subset of the numbers to each available processor, andthen putting the list of positive results back together. In thisspecification, the multiple processing devices that execute theindividual pieces of a parallel program are referred to as ‘computenodes.’ A parallel computer is composed of compute nodes and otherprocessing nodes as well, including, for example, input/output (‘I/O’)nodes, and service nodes.

Parallel algorithms are valuable because it is faster to perform somekinds of large computing jobs via a parallel algorithm than it is via aserial (non-parallel) algorithm, because of the way modern processorswork. It is far more difficult to construct a computer with a singlefast processor than one with many slow processors with the samethroughput. There are also certain theoretical limits to the potentialspeed of serial processors. On the other hand, every parallel algorithmhas a serial part and so parallel algorithms have a saturation point.After that point adding more processors does not yield any morethroughput but only increases the overhead and cost.

Parallel algorithms are designed also to optimize one more resource, thedata communications requirements among the nodes of a parallel computer.There are two ways parallel processors communicate, shared memory ormessage passing. Shared memory processing needs additional locking forthe data and imposes the overhead of additional processor and bus cyclesand also serializes some portion of the algorithm.

Message passing processing uses high-speed data communications networksand message buffers, but this communication adds transfer overhead onthe data communications networks as well as additional memory need formessage buffers and latency in the data communications among nodes.Designs of parallel computers use specially designed data communicationslinks so that the communication overhead will be small but it is theparallel algorithm that decides the volume of the traffic.

Many data communications network architectures are used for messagepassing among nodes in parallel computers. Compute nodes may beorganized in a network as a ‘torus’ or ‘mesh,’ for example. Also,compute nodes may be organized in a network as a tree. A torus networkconnects the nodes in a three-dimensional mesh with wrap around links.Every node is connected to its six neighbors through this torus network,and each node is addressed by its x,y,z coordinate in the mesh. In atree network, the nodes typically are connected into a binary tree: eachnode has a parent and two children (although some nodes may only havezero children or one child, depending on the hardware configuration). Incomputers that use a torus and a tree network, the two networkstypically are implemented independently of one another, with separaterouting circuits, separate physical links, and separate message buffers.

A torus network lends itself to point to point operations, but a treenetwork typically is inefficient in point to point communication. A treenetwork, however, does provide high bandwidth and low latency forcertain collective operations, message passing operations where allcompute nodes participate simultaneously, such as, for example, anallgather.

There is at this time a general trend in computer processor developmentto move from multi-core to many-core processors: from dual-, tri-,quad-, hexa-, octo-core chips to ones with tens or even hundreds ofcores. In addition, multi-core chips mixed with simultaneousmultithreading, memory-on-chip, and special-purpose heterogeneous corespromise further performance and efficiency gains, especially inprocessing multimedia, recognition and networking applications. Thistrend is impacting the supercomputing world as well, where largetransistor count chips are more efficiently used by replicating cores,rather than building chips that are very fast but very inefficient interms of power utilization.

At the same time, the network link speed and number of links into andout of a compute node are dramatically increasing. IBM's BlueGene/Q™supercomputer, for example, will have a five-dimensional torus network,which implements ten bidirectional data communications links per computenode—and BlueGene/Q will support many thousands of compute nodes. Tokeep these links filled with data, DMA engines are employed, butincreasingly, the HPC community is interested in latency. In traditionalsupercomputers with pared-down operating systems, there is little or nomulti-tasking within compute nodes. When a data communications link isunavailable, a task typically blocks or ‘spins’ on a data transmission,in effect, idling a processor until a data transmission resource becomesavailable. In the trend for more powerful individual processors, suchblocking or spinning has a bad effect on latency.

SUMMARY OF THE INVENTION

Methods, parallel computers, and computer program products forendpoint-based parallel data processing in a parallel active messaginginterface (‘PAMI’) of a parallel computer, the parallel computerincluding a plurality of compute nodes that execute a parallelapplication, the PAMI composed of data communications endpoints, eachendpoint including a specification of data communications parameters fora thread of execution on a compute node, including specifications of aclient, a context, and a task, the compute nodes coupled for datacommunications through the PAMI, including establishing by anapplication-level entity, for collective operations of the PAMI, a datacommunications geometry, the geometry specifying, for tasks representingprocesses of execution of the parallel application, a set of endpointsthat are used in collective operations of the PAMI, including aplurality of endpoints for one of the tasks; receiving in one or moreendpoints of the geometry an instruction for a collective operation, theinstruction specifying communications of transfer data among theendpoints of the geometry; and executing the instruction for acollective operation through the endpoints in dependence upon thegeometry, including dividing data communications operations among theplurality of endpoints for one of the tasks.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of example embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of example embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 sets forth a block and network diagram of an example parallelcomputer that implements endpoint-based parallel data processing in aparallel active messaging interface (‘PAMI’) according to embodiments ofthe present invention.

FIG. 2 sets forth a block diagram of an example compute node for use inparallel computers that implement endpoint-based parallel dataprocessing in a PAMI according to embodiments of the present invention.

FIG. 3A illustrates an example Point To Point Adapter for use inparallel computers that implement endpoint-based parallel dataprocessing in a PAMI according to embodiments of the present invention.

FIG. 3B illustrates an example Collective Operations Adapter for use inparallel computers that implement endpoint-based parallel dataprocessing in a PAMI according to embodiments of the present invention.

FIG. 4 illustrates an example data communications network optimized forpoint to point operations for use in parallel computers that implementendpoint-based parallel data processing in a PAMI according toembodiments of the present invention.

FIG. 5 illustrates an example data communications network optimized forcollective operations by organizing compute nodes in a tree for use inparallel computers that implement endpoint-based parallel dataprocessing in a PAMI according to embodiments of the present invention.

FIG. 6 sets forth a block diagram of an example protocol stack for usein parallel computers that implement endpoint-based parallel dataprocessing in a PAMI according to embodiments of the present invention.

FIG. 7 sets forth a functional block diagram of an example PAMI for usein parallel computers that implement endpoint-based parallel dataprocessing in a PAMI according to embodiments of the present invention.

FIG. 8A sets forth a functional block diagram of example datacommunications resources for use in parallel computers that implementendpoint-based parallel data processing in a PAMI according toembodiments of the present invention.

FIG. 8B sets forth a functional block diagram of an example DMAcontroller—in an architecture where the DMA controller is the only DMAcontroller on a compute node—and an origin endpoint and its targetendpoint are both located on the same compute node.

FIG. 9 sets forth a functional block diagram of an example PAMI for usein parallel computers that implement endpoint-based parallel dataprocessing in a PAMI according to embodiments of the present invention.

FIG. 10 sets forth a functional block diagram of example endpoints foruse in parallel computers that implement endpoint-based parallel dataprocessing in a PAMI according to embodiments of the present invention.

FIG. 11 sets forth a flow chart illustrating an example method ofendpoint-based parallel data processing in a PAMI of a parallel computeraccording to embodiments of the present invention.

FIG. 12 sets forth a data flow diagram that illustrates data flowseffected according to the method of FIG. 11.

FIG. 13 sets forth a flow chart illustrating a further example method ofendpoint-based parallel data processing in a PAMI of a parallel computeraccording to embodiments of the present invention.

FIG. 14 sets forth a data flow diagram that illustrates data flowseffected according to the method of FIG. 13.

FIG. 15 sets forth a flow chart illustrating a further example method ofendpoint-based parallel data processing in a PAMI of a parallel computeraccording to embodiments of the present invention.

FIG. 16 sets forth a data flow diagram that illustrates data flowseffected according to the method of FIG. 15.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Example methods, computers, and computer program products forendpoint-based parallel data processing in a parallel active messaginginterface (‘PAMI’) of a parallel computer according to embodiments ofthe present invention are described with reference to the accompanyingdrawings, beginning with FIG. 1. FIG. 1 sets forth a block and networkdiagram of an example parallel computer (100) that implementsendpoint-based parallel data processing in a PAMI according toembodiments of the present invention. The parallel computer (100) in theexample of FIG. 1 is coupled to non-volatile memory for the computer inthe form of data storage device (118), an output device for the computerin the form of printer (120), and an input/output device for thecomputer in the form of computer terminal (122). The parallel computer(100) in the example of FIG. 1 includes a plurality of compute nodes(102).

The parallel computer (100) in the example of FIG. 1 includes aplurality of compute nodes (102). The compute nodes (102) are coupledfor data communications by several independent data communicationsnetworks including a high speed Ethernet network (174), a Joint TestAction Group (‘JTAG’) network (104), a tree network (106) which isoptimized for collective operations, and a torus network (108) which isoptimized point to point operations. Tree network (106) is a datacommunications network that includes data communications links connectedto the compute nodes so as to organize the compute nodes as a tree. Eachdata communications network is implemented with data communicationslinks among the compute nodes (102). The data communications linksprovide data communications for parallel operations among the computenodes of the parallel computer.

In addition, the compute nodes (102) of parallel computer are organizedinto at least one operational group (132) of compute nodes forcollective parallel operations on parallel computer (100). Anoperational group of compute nodes is the set of compute nodes uponwhich a collective parallel operation executes. Collective operationsare implemented with data communications among the compute nodes of anoperational group. Collective operations are those functions thatinvolve all the compute nodes of an operational group. A collectiveoperation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group ofcompute nodes. Such an operational group may include all the computenodes in a parallel computer (100) or a subset all the compute nodes.Collective operations are often built around point to point operations.A collective operation requires that all processes on all compute nodeswithin an operational group call the same collective operation withmatching arguments. A ‘broadcast’ is an example of a collectiveoperations for moving data among compute nodes of an operational group.A ‘reduce’ operation is an example of a collective operation thatexecutes arithmetic or logical functions on data distributed among thecompute nodes of an operational group. An operational group may beimplemented as, for example, an MPI ‘communicator.’

‘MPI’ refers to ‘Message Passing Interface,’ a prior art applicationsmessaging module or parallel communications library, anapplication-level messaging module of computer program instructions fordata communications on parallel computers. Such an application messagingmodule is disposed in an application messaging layer in a datacommunications protocol stack. Examples of prior-art parallelcommunications libraries that may be improved for use with parallelcomputers that implement endpoint-based parallel data processing in aPAMI of a parallel computer according to embodiments of the presentinvention include IBM's MPI library, the ‘Parallel Virtual Machine’(‘PVM’) library, MPICH, OpenMPI, and LAM/MPI. MPI is promulgated by theMPI Forum, an open group with representatives from many organizationsthat define and maintain the MPI standard. MPI at the time of thiswriting is a de facto standard for communication among compute nodesrunning a parallel program on a distributed memory parallel computer.This specification sometimes uses MPI terminology for ease ofexplanation, although the use of MPI as such is not a requirement orlimitation of the present invention.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. In a broadcastoperation, all processes specify the same root process, whose buffercontents will be sent. Processes other than the root specify receivebuffers. After the operation, all buffers contain the message from theroot process.

A scatter operation, like the broadcast operation, is also a one-to-manycollective operation. All processes specify the same receive count. Thesend arguments are only significant to the root process, whose bufferactually contains sendcount*N elements of a given datatype, where N isthe number of processes in the given group of compute nodes. The sendbuffer will be divided equally and dispersed to all processes (includingitself). Each compute node is assigned a sequential identifier termed a‘rank.’ After the operation, the root has sent sendcount data elementsto each process in increasing rank order. Rank 0 receives the firstsendcount data elements from the send buffer. Rank 1 receives the secondsendcount data elements from the send buffer, and so on.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the ranked compute nodes into a receivebuffer in a root node.

A reduce operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from computer node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the root process's receivebuffer. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations. MPI, for example, provides the following pre-definedreduction operations:

MPI_MAX maximum MPI_MIN minimum MPI_SUM sum MPI_PROD product MPI_LANDlogical and MPI_BAND bitwise and MPI_LOR logical or MPI_BOR bitwise orMPI_LXOR logical exclusive or MPI_BXOR bitwise exclusive or

In addition to compute nodes, the example parallel computer (100)includes input/output (‘I/O’) nodes (110, 114) coupled to compute nodes(102) through one of the data communications networks (174). The I/Onodes (110, 114) provide I/O services between compute nodes (102) andI/O devices (118, 120, 122). I/O nodes (110, 114) are connected for datacommunications to I/O devices (118, 120, 122) through local area network(‘LAN’) (130). Computer (100) also includes a service node (116) coupledto the compute nodes through one of the networks (104). Service node(116) provides service common to pluralities of compute nodes, loadingprograms into the compute nodes, starting program execution on thecompute nodes, retrieving results of program operations on the computernodes, and so on. Service node (116) runs a service application (124)and communicates with users (128) through a service applicationinterface (126) that runs on computer terminal (122).

As the term is used here, a parallel active messaging interface or‘PAMI’ (218) is a system-level messaging layer in a protocol stack of aparallel computer that is composed of data communications endpoints eachof which is specified with data communications parameters for a threadof execution on a compute node of the parallel computer. The PAMI is a‘parallel’ interface in that many instances of the PAMI operate inparallel on the compute nodes of a parallel computer. The PAMI is an‘active messaging interface’ in that data communications messages in thePAMI are active messages, ‘active’ in the sense that such messagesimplement callback functions to advise of message dispatch andinstruction completion and so on, thereby reducing the quantity ofacknowledgment traffic, and the like, burdening the data communicationresources of the PAMI.

Each data communications endpoint of a PAMI is implemented as acombination of a client, a context, and a task. A ‘client’ as the termis used in PAMI operations is a collection of data communicationsresources dedicated to the exclusive use of an application-level dataprocessing entity, an application or an application messaging modulesuch as an MPI library. A ‘context’ as the term is used in PAMIoperations is composed of a subset of a client's collection of dataprocessing resources, context functions, and a work queue of datatransfer instructions to be performed by use of the subset through thecontext functions operated by an assigned thread of execution. In atleast some embodiments, the context's subset of a client's dataprocessing resources is dedicated to the exclusive use of the context. A‘task’ as the term is used in PAMI operations refers to a canonicalentity, an integer or objection oriented programming object, thatrepresents in a PAMI a process of execution of the parallel application.That is, a task is typically implemented as an identifier of aparticular instance of an application executing on a compute node, acompute core on a compute node, or a thread of execution on amulti-threading compute core on a compute node.

In the example of FIG. 1, the compute nodes (102), as well as PAMIendpoints on the compute nodes, are coupled for data communicationsthrough the PAMI (218) and through data communications resources such ascollective network (106) and point-to-point network (108). Theimplementation of endpoint-based parallel data processing by theparallel computer of FIG. 1 is further explained with reference to asend buffer (412) of a root task, a set of transfer data (384), a datacommunications geometry (404), a set of endpoints (378) of the geometry,and a plurality (376) of endpoints for one task. The parallel computer(100) of FIG. 1 operates generally to carry out endpoint-based paralleldata processing in a PAMI of a parallel computer according toembodiments of the present invention by establishing by anapplication-level entity, that is, an application as such or anapplication messaging module, for collective operations of the PAMI(218), a data communications geometry (303). The geometry specifies, fortasks representing processes of execution of the parallel application, aset of endpoints (378) that are used in collective operations of thePAMI, including a plurality (376) of endpoints for one of the tasks.Other PAMI tasks can have one or more endpoints in the geometry (404),but at least one of the tasks has a plurality of endpoints in thegeometry. The parallel computer (100) of FIG. 1 further operatesgenerally to carry out endpoint-based parallel data processing in a PAMIby receiving in one or more endpoints, in this example, endpoint (352),of the geometry an instruction (390) for a collective operation. Theinstruction specifies communications of transfer data (384) among theendpoints of the geometry, in this example, from a send buffer (412) ofa root task as in a BROADCAST or SCATTER collective operation. Theinstruction (390) is receives as a post by application or applicationmessaging module (158) into a work queue of a context of the endpoint(352). The parallel computer (100) of FIG. 1 further operates generallyto carry out endpoint-based parallel data processing in a PAMI byexecuting the instruction (390) through the endpoints (352, 376, 378) independence upon the geometry, including dividing data communicationsoperations among the plurality (376) of endpoints for one of the tasks.

Examples of instruction types include SEND instructions for datatransfers through networks, PUT instructions for data transfers throughDMA, GET instructions for data transfer through segments of sharedmemory, and others. Data communications instructions, includinginstructions for collective operations, processed by the parallelcomputer here can include both eager data communications instructions,receive instructions, DMA PUT instructions, DMA GET instructions, and soon. Some data communications instructions, typically GETs and PUTs areone-sided DMA instructions in that there is no cooperation required froma target processor, no computation on the target side to complete such aPUT or GET because data is transferred directly to or from memory on theother side of the transfer. In this setting, the term ‘target’ is usedfor either PUT or GET. A PUT target receives data directly into its RAMfrom an origin endpoint. A GET target provides data directly from itsRAM to the origin endpoint. Thus readers will recognize that thedesignation of an endpoint as an origin endpoint for a transfer is adesignation of the endpoint that initiates execution of a DMA transferinstruction—rather than a designation of the direction of the transfer:PUT instructions transfer data from an origin endpoint to a targetendpoint. GET instructions transfer data from a target endpoint to anorigin endpoint.

In any particular communication of data, an origin endpoint and a targetendpoint can be any two endpoints on any of the compute nodes (102), ondifferent compute nodes, or two endpoints on the same compute node.Collective operations can have one origin endpoint and many targetendpoints, as in a BROADCAST, for example, or many origin endpoints andone target endpoint, as in a GATHER, for example. A sequence of datacommunications instructions, including instructions for collectiveoperations, resides in a work queue of a context and results in datatransfers among endpoints, origin endpoints and target endpoints. Datacommunications instructions, including instructions for collectiveoperations, are ‘active’ in the sense that the instructions implementcallback functions to advise of and implement instruction dispatch andinstruction completion, thereby reducing the quantity of acknowledgmenttraffic required on the network. Each such data communicationsinstruction or instruction for a collective operation effects a datatransfer or transfers, from one or more origin endpoints to one or moretarget endpoints, through some form of data communications resources,networks, shared memory segments, network adapters, DMA controllers, andthe like.

The arrangement of compute nodes, networks, and I/O devices making upthe example parallel computer illustrated in FIG. 1 are for explanationonly, not for limitation of the present invention. Parallel computerscapable of data communications in a PAMI according to embodiments of thepresent invention may include additional nodes, networks, devices, andarchitectures, not shown in FIG. 1, as will occur to those of skill inthe art. For ease of explanation, the parallel computer in the exampleof FIG. 1 is illustrated with only one data buffer (412), one set oftransfer data (384), and only a few endpoints (376, 378); readers willrecognize, however, that practical embodiments of such a parallelcomputer will include many buffers, send buffers and receive buffers,many sets of transfer data, and many endpoints. The parallel computer(100) in the example of FIG. 1 includes sixteen compute nodes (102);some parallel computers that implement endpoint-based parallel dataprocessing in a PAMI according to some embodiments of the presentinvention include thousands of compute nodes. In addition to Ethernetand JTAG, networks in such data processing systems may support many datacommunications protocols including for example TCP (Transmission ControlProtocol), IP (Internet Protocol), and others as will occur to those ofskill in the art. Various embodiments of the present invention may beimplemented on a variety of hardware platforms in addition to thoseillustrated in FIG. 1.

Endpoint-based parallel data processing in a PAMI according toembodiments of the present invention is generally implemented on aparallel computer that includes a plurality of compute nodes. In fact,such computers may include thousands of such compute nodes, with acompute node typically executing at least one instance of a parallelapplication. Each compute node is in turn itself a computer composed ofone or more computer processors, its own computer memory, and its owninput/output (‘I/O’) adapters. For further explanation, therefore, FIG.2 sets forth a block diagram of an example compute node (152) for use ina parallel computer that implement endpoint-based parallel dataprocessing in a PAMI according to embodiments of the present invention.The compute node (152) of FIG. 2 includes one or more computerprocessors (164) as well as random access memory (‘RAM’) (156). Eachprocessor (164) can support multiple hardware compute cores (165), andeach such core can in turn support multiple threads of execution,hardware threads of execution as well as software threads. Eachprocessor (164) is connected to RAM (156) through a high-speed frontside bus (161), bus adapter (194), and a high-speed memory bus (154)—andthrough bus adapter (194) and an extension bus (168) to other componentsof the compute node. Stored in RAM (156) is an application program(158), a module of computer program instructions that carries outparallel, user-level data processing using parallel algorithms.

Also stored RAM (156) is an application messaging module (216), alibrary of computer program instructions that carry outapplication-level parallel communications among compute nodes, includingpoint to point operations as well as collective operations. Although theapplication program can call PAMI routines directly, the applicationprogram (158) often executes point-to-point data communicationsoperations by calling software routines in the application messagingmodule (216), which in turn is improved according to embodiments of thepresent invention to use PAMI functions to implement suchcommunications. An application messaging module can be developed fromscratch to use a PAMI according to embodiments of the present invention,using a traditional programming language such as the C programminglanguage or C++, for example, and using traditional programming methodsto write parallel communications routines that send and receive dataamong PAMI endpoints and compute nodes through data communicationsnetworks or shared-memory transfers. In this approach, the applicationmessaging module (216) exposes a traditional interface, such as MPI, tothe application program (158) so that the application program can gainthe benefits of a PAMI with no need to recode the application. As analternative to coding from scratch, therefore, existing prior artapplication messaging modules may be improved to use the PAMI, existingmodules that already implement a traditional interface. Examples ofprior-art application messaging modules that can be improved toimplement endpoint-based parallel data processing in a PAMI according toembodiments of the present invention include such parallelcommunications libraries as the traditional ‘Message Passing Interface’(‘MPI’) library, the ‘Parallel Virtual Machine’ (‘PVM’) library, MPICH,and the like.

Also represented in RAM in the example of FIG. 2 is a PAMI (218).Readers will recognize, however, that the representation of the PAMI inRAM is a convention for ease of explanation rather than a limitation ofthe present invention, because the PAMI and its components, endpoints,clients, contexts, and so on, have particular associations with andinclusions of hardware data communications resources. In fact, the PAMIcan be implemented partly as software or firmware and hardware—or even,at least in some embodiments, entirely in hardware.

Also represented in RAM (156) in the example of FIG. 2 is a segment(227) of shared memory. In typical operation, the operating system (162)in this example compute node assigns portions of address space to eachprocessor (164), and, to the extent that the processors include multiplecompute cores (165), treats each compute core as a separate processorwith its own assignment of a portion of core memory or RAM (156) for aseparate heap, stack, memory variable storage, and so on. The defaultarchitecture for such apportionment of memory space is that eachprocessor or compute core operates its assigned portion of memoryseparately, with no ability to access memory assigned to anotherprocessor or compute core. Upon request, however, the operating systemgrants to one processor or compute core the ability to access a segmentof memory that is assigned to another processor or compute core, andsuch a segment is referred to in this specification as a ‘segment ofshared memory.’

In the example of FIG. 2, each processor or compute core has uniformaccess to the RAM (156) on the compute node, so that accessing a segmentof shared memory is equally fast regardless where the shared segment islocated in physical memory. In some embodiments, however, modules ofphysical memory are dedicated to particular processors, so that aprocessor may access local memory quickly and remote memory more slowly,a configuration referred to as a Non-Uniform Memory Access or ‘NUMA.’ Insuch embodiments, a segment of shared memory can be configured locallyfor one endpoint and remotely for another endpoint—or remotely from bothendpoints of a communication. From the perspective of an origin endpointtransmitting data through a segment of shared memory that is configuredremotely with respect to the origin endpoint, transmitting data throughthe segment of shared memory will appear slower that if the segment ofshared memory were configured locally with respect to the originendpoint—or if the segment were local to both the origin endpoint andthe target endpoint. This is the effect of the architecture representedby the compute node (152) in the example of FIG. 2 with all processorsand all compute cores coupled through the same bus to the RAM—that allaccesses to segments of memory shared among processes or processors onthe compute node are local—and therefore very fast.

Also stored in RAM (156) in the example compute node of FIG. 2 is anoperating system (162), a module of computer program instructions androutines for an application program's access to other resources of thecompute node. It is possible, in some embodiments at least, for anapplication program, an application messaging module, and a PAMI in acompute node of a parallel computer to run threads of execution with nouser login and no security issues because each such thread is entitledto complete access to all resources of the node. The quantity andcomplexity of duties to be performed by an operating system on a computenode in a parallel computer therefore can be somewhat smaller and lesscomplex than those of an operating system on a serial computer with manythreads running simultaneously with various level of authorization foraccess to resources. In addition, there is no video I/O on the computenode (152) of FIG. 2, another factor that decreases the demands on theoperating system. The operating system may therefore be quitelightweight by comparison with operating systems of general purposecomputers, a pared down or ‘lightweight’ version as it were, or anoperating system developed specifically for operations on a particularparallel computer. Operating systems that may be improved or simplifiedfor use in a compute node according to embodiments of the presentinvention include UNIX™, Linux™, Microsoft XP™, AIX™, IBM's i5/OS™, andothers as will occur to those of skill in the art.

The example compute node (152) of FIG. 2 includes several communicationsadapters (172, 176, 180, 188) for implementing data communications withother nodes of a parallel computer. Such data communications may becarried out serially through RS-232 connections, through external busessuch as USB, through data communications networks such as IP networks,and in other ways as will occur to those of skill in the art.Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a network. Examples ofcommunications adapters for use in computers that implementendpoint-based parallel data processing in a parallel active messaginginterface (‘PAMI’) according to embodiments of the present inventioninclude modems for wired communications, Ethernet (IEEE 802.3) adaptersfor wired network communications, and 802.11b adapters for wirelessnetwork communications.

The data communications adapters in the example of FIG. 2 include aGigabit Ethernet adapter (172) that couples example compute node (152)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 2 includes aJTAG Slave circuit (176) that couples example compute node (152) fordata communications to a JTAG Master circuit (178). JTAG is the usualname for the IEEE 1149.1 standard entitled Standard Test Access Port andBoundary-Scan Architecture for test access ports used for testingprinted circuit boards using boundary scan. JTAG is so widely adaptedthat, at this time, boundary scan is more or less synonymous with JTAG.JTAG is used not only for printed circuit boards, but also forconducting boundary scans of integrated circuits, and is also used as amechanism for debugging embedded systems, providing a convenient “backdoor” into the system. The example compute node of FIG. 2 may be allthree of these: It typically includes one or more integrated circuitsinstalled on a printed circuit board and may be implemented as anembedded system having its own processor, its own memory, and its ownI/O capability. JTAG boundary scans through JTAG Slave (176) mayefficiently configure processor registers and memory in compute node(152) for use in data communications in a PAMI according to embodimentsof the present invention.

The data communications adapters in the example of FIG. 2 includes aPoint To Point Adapter (180) that couples example compute node (152) fordata communications to a data communications network (108) that isoptimal for point to point message passing operations such as, forexample, a network configured as a three-dimensional torus or mesh.Point To Point Adapter (180) provides data communications in sixdirections on three communications axes, x, y, and z, through sixbidirectional links: +x (181), −x (182), +y (183), −y (184), +z (185),and −z (186). For ease of explanation, the Point To Point Adapter (180)of FIG. 2 as illustrated is configured for data communications in threedimensions, x, y, and z, but readers will recognize that Point To PointAdapters optimized for point-to-point operations in data communicationsin a PAMI of a parallel computer according to embodiments of the presentinvention may in fact be implemented so as to support communications intwo dimensions, four dimensions, five dimensions, and so on.

The data communications adapters in the example of FIG. 2 includes aCollective Operations Adapter (188) that couples example compute node(152) for data communications to a network (106) that is optimal forcollective message passing operations such as, for example, a networkconfigured as a binary tree. Collective Operations Adapter (188)provides data communications through three bidirectional links: two tochildren nodes (190) and one to a parent node (192).

The example compute node (152) includes a number of arithmetic logicunits (‘ALUs’). ALUs (166) are components of processors (164), and aseparate ALU (170) is dedicated to the exclusive use of collectiveoperations adapter (188) for use in performing the arithmetic andlogical functions of reduction operations. Computer program instructionsof a reduction routine in an application messaging module (216) or aPAMI (218) may latch an instruction for an arithmetic or logicalfunction into instruction register (169). When the arithmetic or logicalfunction of a reduction operation is a ‘sum’ or a ‘logical OR,’ forexample, collective operations adapter (188) may execute the arithmeticor logical operation by use of an ALU (166) in a processor (164) or,typically much faster, by use of the dedicated ALU (170).

The example compute node (152) of FIG. 2 includes a direct memory access(‘DMA’) controller (225), a module of automated computing machinery thatimplements, through communications with other DMA engines on othercompute nodes, or on a same compute node, direct memory access to andfrom memory on its own compute node as well as memory on other computenodes. Direct memory access is a way of reading and writing to and frommemory of compute nodes with reduced operational burden on computerprocessors (164); a CPU initiates a DMA transfer, but the CPU does notexecute the DMA transfer. A DMA transfer essentially copies a block ofmemory from one compute node to another, or between RAM segments ofapplications on the same compute node, from an origin to a target for aPUT operation, from a target to an origin for a GET operation.

For further explanation, FIG. 3A illustrates an example of a Point ToPoint Adapter (180) useful in parallel computers that implementendpoint-based parallel data processing in a PAMI according toembodiments of the present invention. Point To Point Adapter (180) isdesigned for use in a data communications network optimized for point topoint operations, a network that organizes compute nodes in athree-dimensional torus or mesh. Point To Point Adapter (180) in theexample of FIG. 3A provides data communication along an x-axis throughfour unidirectional data communications links, to and from the next nodein the −x direction (182) and to and from the next node in the +xdirection (181). Point To Point Adapter (180) also provides datacommunication along a y-axis through four unidirectional datacommunications links, to and from the next node in the −y direction(184) and to and from the next node in the +y direction (183). Point ToPoint Adapter (180) in also provides data communication along a z-axisthrough four unidirectional data communications links, to and from thenext node in the −z direction (186) and to and from the next node in the+z direction (185). For ease of explanation, the Point To Point Adapter(180) of FIG. 3A as illustrated is configured for data communications inonly three dimensions, x, y, and z, but readers will recognize thatPoint To Point Adapters optimized for point-to-point operations in aparallel computer that implements endpoint-based parallel dataprocessing according to embodiments of the present invention may in factbe implemented so as to support communications in two dimensions, fourdimensions, five dimensions, and so on. Several supercomputers now usefive dimensional mesh or torus networks, including, for example, IBM'sBlue Gene Q™.

For further explanation, FIG. 3B illustrates an example of a CollectiveOperations Adapter (188) useful in a parallel computer that implementsendpoint-based parallel data processing in a PAMI according toembodiments of the present invention. Collective Operations Adapter(188) is designed for use in a network optimized for collectiveoperations, a network that organizes compute nodes of a parallelcomputer in a binary tree. Collective Operations Adapter (188) in theexample of FIG. 3B provides data communication to and from two childrennodes through four unidirectional data communications links (190).Collective Operations Adapter (188) also provides data communication toand from a parent node through two unidirectional data communicationslinks (192).

For further explanation, FIG. 4 sets forth a line drawing illustratingan example data communications network (108) optimized forpoint-to-point operations useful in parallel computers that implementendpoint-based parallel data processing in a PAMI according toembodiments of the present invention. In the example of FIG. 4, dotsrepresent compute nodes (102) of a parallel computer, and the dottedlines between the dots represent data communications links (103) betweencompute nodes. The data communications links are implemented withpoint-to-point data communications adapters similar to the oneillustrated for example in FIG. 3A, with data communications links onthree axis, x, y, and z, and to and fro in six directions +x (181), −x(182), +y (183), −y (184), +z (185), and −z (186). The links and computenodes are organized by this data communications network optimized forpoint-to-point operations into a three dimensional mesh (105). The mesh(105) has wrap-around links on each axis that connect the outermostcompute nodes in the mesh (105) on opposite sides of the mesh (105).These wrap-around links form a torus (107). Each compute node in thetorus has a location in the torus that is uniquely specified by a set ofx, y, z coordinates. Readers will note that the wrap-around links in they and z directions have been omitted for clarity, but are configured ina similar manner to the wrap-around link illustrated in the x direction.For clarity of explanation, the data communications network of FIG. 4 isillustrated with only 27 compute nodes, but readers will recognize thata data communications network optimized for point-to-point operations ina parallel computer that implements endpoint-based parallel dataprocessing according to embodiments of the present invention may containonly a few compute nodes or may contain thousands of compute nodes. Forease of explanation, the data communications network of FIG. 4 isillustrated with only three dimensions: x, y, and z, but readers willrecognize that a data communications network optimized forpoint-to-point operations may in fact be implemented in two dimensions,four dimensions, five dimensions, and so on. As mentioned, severalsupercomputers now use five dimensional mesh or torus networks,including IBM's Blue Gene Q™.

For further explanation, FIG. 5 illustrates an example datacommunications network (106) optimized for collective operations byorganizing compute nodes in a tree. The example data communicationsnetwork of FIG. 5 includes data communications links connected to thecompute nodes so as to organize the compute nodes as a tree. In theexample of FIG. 5, dots represent compute nodes (102) of a parallelcomputer, and the dotted lines (103) between the dots represent datacommunications links between compute nodes. The data communicationslinks are implemented with collective operations data communicationsadapters similar to the one illustrated for example in FIG. 3B, witheach node typically providing data communications to and from twochildren nodes and data communications to and from a parent node, withsome exceptions. Nodes in a binary tree may be characterized as a rootnode (202), branch nodes (204), and leaf nodes (206). The root node(202) has two children but no parent. The leaf nodes (206) each has aparent, but leaf nodes have no children. The branch nodes (204) each hasboth a parent and two children. The links and compute nodes are therebyorganized by this data communications network optimized for collectiveoperations into a binary tree (106). For clarity of explanation, thedata communications network of FIG. 5 is illustrated with only 31compute nodes, but readers will recognize that a data communicationsnetwork optimized for collective operations for use in parallelcomputers that implement endpoint-based parallel data processing in aPAMI according to embodiments of the present invention may contain onlya few compute nodes or hundreds or thousands of compute nodes.

In the example of FIG. 5, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (250). The rank actually identifiesan instance of a parallel application that is executing on a computenode. That is, the rank is an application-level identifier. Using therank to identify a node assumes that only one such instance of anapplication is executing on each node. A compute node can, however,support multiple processors, each of which can support multipleprocessing cores—so that more than one process or instance of anapplication can easily be present under execution on any given computenode—or in all the compute nodes, for that matter. To the extent thatmore than one instance of an application executes on a single computenode, the rank identifies the instance of the application as such ratherthan the compute node. A rank uniquely identifies an application'slocation in the tree network for use in both point-to-point andcollective operations in the tree network. The ranks in this example areassigned as integers beginning with ‘0’ assigned to the root instance orroot node (202), ‘1’ assigned to the first node in the second layer ofthe tree, ‘2’ assigned to the second node in the second layer of thetree, ‘3’ assigned to the first node in the third layer of the tree, ‘4’assigned to the second node in the third layer of the tree, and so on.For ease of illustration, only the ranks of the first three layers ofthe tree are shown here, but all compute nodes, or rather allapplication instances, in the tree network are assigned a unique rank.Such rank values can also be assigned as identifiers of applicationinstances as organized in a mesh or torus network.

For further explanation, FIG. 6 sets forth a block diagram of an exampleprotocol stack useful in parallel computers that implementendpoint-based parallel data processing in a PAMI according toembodiments of the present invention. The example protocol stack of FIG.6 includes a hardware layer (214), a system messaging layer (212), anapplication messaging layer (210), and an application layer (208). Forease of explanation, the protocol layers in the example stack of FIG. 6are shown connecting an origin compute node (222) and a target computenode (224), although it is worthwhile to point out that in embodimentsthat effect DMA data transfers, the origin compute node and the targetcompute node can be the same compute node. The granularity of connectionthrough the system messaging layer (212), which is implemented with aPAMI (218), is finer than merely compute node to compute node—because,again, communications among endpoints often is communications amongendpoints on the same compute node. For further explanation, recall thatthe PAMI (218) connects endpoints, connections specified by combinationsof clients, contexts, and tasks, each such combination being specific toa thread of execution on a compute node, with each compute node capableof supporting many threads and therefore many endpoints. Every endpointtypically can function as both an origin endpoint or a target endpointfor data transfers through a PAMI, and both the origin endpoint and itstarget endpoint can be located on the same compute node. So an origincompute node (222) and its target compute node (224) can in fact, andoften will, be the same compute node.

The application layer (208) provides communications among instances of aparallel application (158) running on the compute nodes (222, 224) byinvoking functions in an application messaging module (216) installed oneach compute node. Communications among instances of the applicationthrough messages passed between the instances of the application.Applications may communicate messages invoking function of anapplication programming interface (‘API’) exposed by the applicationmessaging module (216). In this approach, the application messagingmodule (216) exposes a traditional interface, such as an API of an MPIlibrary, to the application program (158) so that the applicationprogram can gain the benefits of a PAMI, reduced network traffic,callback functions, and so on, with no need to recode the application.Alternatively, if the parallel application is programmed to use PAMIfunctions, the application can call the PAMI functions directly, withoutgoing through the application messaging module.

The example protocol stack of FIG. 6 includes a system messaging layer(212) implemented here as a PAMI (218). The PAMI provides system-leveldata communications functions that support messaging in the applicationlayer (602) and the application messaging layer (610). Such system-levelfunctions are typically invoked through an API exposed to theapplication messaging modules (216) in the application messaging layer(210). Although developers can in fact access a PAMI API directly bycoding an application to do so, a PAMI's system-level functions in thesystem messaging layer (212) in many embodiments are isolated from theapplication layer (208) by the application messaging layer (210), makingthe application layer somewhat independent of system specific details.With an application messaging module presenting a standard MPI API to anapplication, for example, with the application messaging module retooledto use the PAMI to carry out the low-level messaging functions, theapplication gains the benefits of a PAMI with no need to incur theexpense of reprogramming the application to call the PAMI directly.Because, however, some applications will in fact be reprogrammed to callthe PAMI directly, all entities in the protocol stack above the PAMI areviewed by PAMI as applications. When PAMI functions are invoked byentities above the PAMI in the stack, the PAMI makes no distinctionwhether the caller is in the application layer or the applicationmessaging layer, no distinction whether the caller is an application assuch or an MPI library function invoked by an application. As far as thePAMI is concerned, any caller of a PAMI function is an application.

The protocol stack of FIG. 6 includes a hardware layer (634) thatdefines the physical implementation and the electrical implementation ofaspects of the hardware on the compute nodes such as the bus, networkcabling, connector types, physical data rates, data transmissionencoding and many other factors for communications between the computenodes (222) on the physical network medium. In parallel computers thatimplement endpoint-based parallel data processing with DMA controllersaccording to embodiments of the present invention, the hardware layerincludes DMA controllers and network links, including routers, packetswitches, and the like.

For further explanation, FIG. 7 sets forth a functional block diagram ofan example PAMI (218) for use in parallel computers that implementendpoint-based parallel data processing in a PAMI according toembodiments of the present invention. The PAMI (218) provides an activemessaging layer that supports both point to point communications in amesh or torus as well as collective operations, gathers, reductions,barriers, and the like in tree networks, for example. The PAMI is amultithreaded parallel communications engine designed to provide lowlevel message passing functions, many of which are one-sided, andabstract such functions for higher level messaging middleware, referredto in this specification as ‘application messaging modules’ in anapplication messaging layer. In the example of FIG. 7, the applicationmessaging layer is represented by a generic MPI module (258),appropriate for ease of explanation because some form of MPI is a defacto standard for such messaging middleware. Compute nodes andcommunications endpoints of a parallel computer (102 on FIG. 1) arecoupled for data communications through such a PAMI and through datacommunications resources (294, 296, 314) that include DMA controllers,network adapters, and data communications networks through whichcontrollers and adapters deliver data communications.

The PAMI (218) provides data communications among data communicationsendpoints, where each endpoint is specified by data communicationsparameters for a thread of execution on a compute node, includingspecifications of a client, a context, and a task. In the particularexample of FIG. 10, application (158) has mapped thread (251) to advancework on context (290) of endpoint (338), and the combination of client(302), task (332), and context (290) effectively specify datacommunications parameters for thread (251). Similarly, application (158)has mapped thread (252) to advance work on context (292) of endpoint(340), and the combination of client (303), task (333), and context(292) effectively specify data communications parameters for thread(252). Application (157) has mapped thread (253) to advance work oncontext (310) of endpoint (342), and the combination of client (304),task (334), and context (310) effectively specify data communicationsparameters for thread (253). And application (159) has mapped thread(254) to advance work on context (312) of endpoint (344), and thecombination of client (305), task (336), and context (312) effectivelyspecify data communications parameters for thread (254). In theseexamples, a separate thread is assigned to advance work on each contextof an endpoint, but that is not a requirement of the invention. Inembodiments, one thread can advance work on more than one context orendpoint, and more than one thread can be permitted to post work to asame context of an endpoint.

The PAMI (218) in this example includes PAMI clients (302, 304), tasks(286, 298), contexts (190, 292, 310, 312), and endpoints (288, 300). APAMI client is a collection of data communications resources (294, 295,314) dedicated to the exclusive use of an application-level dataprocessing entity, an application or an application messaging modulesuch as an MPI library. Data communications resources assigned incollections to PAMI clients are explained in more detail below withreference to FIGS. 8A and 8B. PAMI clients (203, 304 on FIG. 7) enablehigher level middleware, application messaging modules, MPI libraries,and the like, to be developed independently so that each can be usedconcurrently by an application. Although the application messaging layerin FIG. 7 is represented for example by a single generic MPI module(258), in fact, a PAMI, operating multiple clients, can support multiplemessage passing libraries or application messaging modulessimultaneously, a fact that is explained in more detail with referenceto FIG. 9. FIG. 9 sets forth a functional block diagram of an examplePAMI (218) useful in parallel computers that implement endpoint-basedparallel data processing in a PAMI according to embodiments of thepresent invention in which the example PAMI operates, on behalf of anapplication (158), with multiple application messaging modules (502-510)simultaneously. The application (158) can have multiple messages intransit simultaneously through each of the application messaging modules(502-510). Each context (512-520) carries out, through post and advancefunctions, data communications for the application on datacommunications resources in the exclusive possession, in each client, ofthat context. Each context carries out data communications operationsindependently and in parallel with other contexts in the same or otherclients. In the example FIG. 9, each client (532-540) includes acollection of data communications resources (522-530) dedicated to theexclusive use of an application-level data processing entity, one of theapplication messaging modules (502-510):

-   -   IBM MPI Library (502) operates through context (512) data        communications resources (522) dedicated to the use of PAMI        client (532),    -   MPICH Library (504) operates through context (514) data        communications resources (524) dedicated to the use of PAMI        client (534),    -   Unified Parallel C (‘UPC’) Library (506) operates through        context (516) data communications resources (526) dedicated to        the use of PAMI client (536),    -   Partitioned Global Access Space (‘PGAS’) Runtime Library (508)        operates through context (518) data communications resources        (528) dedicated to the use of PAMI client (538), and    -   Aggregate Remote Memory Copy Interface (‘ARMCI’) Library (510)        operates through context (520) data communications resources        (530) dedicated to the use of PAMI client (540).

Again referring to the example of FIG. 7: The PAMI (218) includes tasks,listed in task lists (286, 298) and identified (250) to the application(158). A ‘task’ as the term is used in PAMI operations is aplatform-defined integer datatype that identifies a canonicalapplication process, an instance of a parallel application (158). Verycarefully in this specification, the term ‘task’ is always used to referonly to this PAMI structure, not the traditional use of the computerterm ‘task’ to refer to a process or thread of execution. In thisspecification, the term ‘process’ refers to a canonical data processingprocess, a container for threads in a multithreading environment. Inparticular in the example of FIG. 7, the application (158) isimplemented as a canonical process with multiple threads (251-254)assigned various duties by a leading thread (251) which itself executesan instance of a parallel application program. Each instance of aparallel application is assigned a task; each task so assigned can be aninteger value, for example, in a C environment, or a separate taskobject in a C++ or Java environment. The tasks are components ofcommunications endpoints, but are not themselves communicationsendpoints; tasks are not addressed directly for data communications inPAMI. This gives a finer grained control than was available in priormessage passing art. Each client has its own list (286, 298) of tasksfor which its contexts provide services; this allows each process topotentially reside simultaneously in two or more differentcommunications domains as will be the case in certain advanced computersusing, for example, one type of processor and network in one domain anda completely different processor type and network in another domain, allin the same computer.

The PAMI (218) includes contexts (290, 292, 310, 312). A ‘context’ asthe term is used in PAMI operations is composed of a subset of aclient's collection of data processing resources, context functions, anda work queue of data transfer instructions to be performed by use of thesubset through the context functions operated by an assigned thread ofexecution. That is, a context represents a partition of the local datacommunications resources assigned to a PAMI client. Every context withina client has equivalent functionality and semantics. Context functionsimplement contexts as threading points that applications use to optimizeconcurrent communications. Communications initiated by a local process,an instance of a parallel application, uses a context object to identifythe specific threading point that will be used to issue a particularcommunication independent of communications occurring in other contexts.In the example of FIG. 7, where the application (158) and theapplication messaging module (258) are both implemented as canonicalprocesses with multiple threads of execution, each has assigned ormapped particular threads (253, 254, 262, 264) to advance (268, 270,276, 278) work on the contexts (290, 292, 310, 312), including executionof local callbacks (272, 280). In particular, the local event callbackfunctions (272, 280) associated with any particular communication areinvoked by the thread advancing the context that was used to initiatethe communication operation in the first place. Like PAMI tasks,contexts are not used to directly address a communication destination ortarget, as they are a local resource.

Context functions, explained here with regard to references (472-482) onFIG. 9, include functions to create (472) and destroy (474) contexts,functions to lock (476) and unlock (478) access to a context, andfunctions to post (480) and advance (480) work in a context. For ease ofexplanation, the context functions (472-482) are illustrated in only oneexpanded context (512); readers will understand, however, that all PAMIcontexts have similar context functions. The create (472) and destroy(474) functions are, in an object-oriented sense, constructors anddestructors. In the example embodiments described in thisspecifications, post (480) and advance (482) functions on a context arecritical sections, not thread safe. Applications using suchnon-reentrant functions must somehow ensure that critical sections areprotected from re-entrant use. Applications can use mutual exclusionlocks to protect critical sections. The lock (476) and unlock (478)functions in the example of FIG. 9 provide and operate such a mutualexclusion lock to protect the critical sections in the post (480) andadvance (482) functions. If only a single thread posts or advances workon a context, then that thread need never lock that context. To theextent that progress is driven independently on a context by a singlethread of execution, then no mutual exclusion locking of the contextitself is required—provided that no other thread ever attempts to call afunction on such a context. If more than one thread will post or advancework on a context, each such thread must secure a lock before calling apost or an advance function on that context. This is one reason why itis probably a preferred architecture, given sufficient resources, toassign one thread to operate each context. Progress can be driven withadvance (482) functions concurrently among multiple contexts by usingmultiple threads, as desired by an application—shown in the example ofFIG. 7 by threads (253, 254, 262, 264) which advance work concurrently,independently and in parallel, on contexts (290, 292, 310, 312).

Posts and advances (480, 482 on FIG. 9) are functions called on acontext, either in a C-type function with a context ID as a parameter,or in object oriented practice where the calling entity possesses areference to a context or a context object as such and the posts andadvances are member methods of a context object. Again referring to FIG.7: Application-level entities, application programs (158) andapplication messaging modules (258), post (266, 274) data communicationsinstructions, including SENDs, RECEIVEs, PUTs, GETs, and so on, to thework queues (282, 284, 306, 308) in contexts and then call advancefunctions (268, 270, 276, 278) on the contexts to progress specific dataprocessing and data communications that carry out the instructions. Thedata processing and data communications effected by the advancefunctions include specific messages, request to send (‘RTS’) messages,acknowledgments, callback execution, transfers of transfer data orpayload data, and so on. Advance functions therefore operate generallyby checking a work queue for any new instructions that need to beinitiated and checking data communications resources for any incomingmessage traffic that needs to be administered as well as increases instorage space available for outgoing message traffic, with callbacks andthe like. Advance functions also carry out or trigger transfers oftransfer data or payload data.

In at least some embodiments, a context's subset of a client's dataprocessing resources is dedicated to the exclusive use of the context.In the example of FIG. 7, context (290) has a subset (294) of a client's(302) data processing resources dedicated to the exclusive use of thecontext (290), and context (292) has a subset (296) of a client's (302)data processing resources dedicated to the exclusive use of the context(292). Advance functions (268, 270) called on contexts (290, 292)therefore never need to secure a lock on a data communications resourcebefore progressing work on a context—because each context (290, 292) hasexclusive use of dedicated data communications resources. Usage of datacommunications resources in this example PAMI (218), however, is notthread-safe. When data communications resources are shared amongcontexts, mutual exclusion locks are needed. In contrast to theexclusive usage of resources by contexts (290, 292), contexts (310, 312)share access to their client's data communications resource (314) andtherefore do not have data communications resources dedicated toexclusive use of a single context. Contexts (310, 312) therefore alwaysmust secure a mutual exclusion lock on a data communications resourcebefore using the resource to send or receive administrative messages ortransfer data.

For further explanation, here is an example pseudocode Hello Worldprogram for an application using a PAMI:

int main(int argc, char ** argv) { PAMI_client_t client; PAMI_context_tcontext; PAMI_result_t status = PAMI_ERROR; const char *name = “PAMI”;status  = PAMI_Client_initialize(name, &client); size_t_n = 1; status  =PAMI_Context_createv(client, NULL, 0, &context, _n);PAMI_configuration_t configuration; configuration.name = PAMI_TASK_ID;status = PAMI_Configuration_query(client, &configuration); size_ttask_id = configuration.value.intval; configuration.name =PAMI_NUM_TASKS; status = PAMI_Configuration_query(client,&configuration); size_t num_tasks = configuration.value.intval; fprintf(stderr, “Hello process %d of %d\n”, task_id, num_tasks); status =PAMI_Context_destroy(context); status = PAMI_Client_finalize(client);return 0; }

This short program is termed ‘pseudocode’ because it is an explanationin the form of computer code, not a working model, not an actual programfor execution. In this pseudocode example, an application initializes aclient and a context for an application named “PAMI.”PAMI_Client_initialize and PAMI_Context_createv are initializationfunctions (316) exposed to applications as part of a PAMI's API. Thesefunctions, in dependence upon the application name “PAMI,” pull from aPAMI configuration (318) the information needed to establish a clientand a context for the application. The application uses this segment:

PAMI_configuration_t configuration; configuration.name = PAMI_TASK_ID;status = PAMI_Configuration_query(client, &configuration); size_ttask_id = configuration.value.intval;to retrieve its task ID and this segment:

configuration.name = PAMI_NUM_TASKS; status =PAMI_Configuration_query(client, &configuration); size_t num_tasks =configuration.value.intval;to retrieve the number of tasks presently configured to carry outparallel communications and implement endpoint-based parallel dataprocessing in the PAMI. The applications prints “Hello process task_idof num_tasks,” where task_id is the task ID of the subject instance of aparallel application, and num_tasks is the number of instances of theapplication executing in parallel on compute nodes. Finally, theapplication destroys the context and terminates the client.

For further explanation of data communications resources assigned incollections to PAMI clients, FIG. 8A sets forth a block diagram ofexample data communications resources (220) useful in parallel computersthat implement endpoint-based parallel data processing in a PAMIaccording to embodiments of the present invention. The datacommunications resources of FIG. 8A include a gigabit Ethernet adapter(238), an Infiniband adapter (240), a Fibre Channel adapter (242), a PCIExpress adapter (246), a collective operations network configured as atree (106), shared memory (227), DMA controllers (225, 226), and anetwork (108) configured as a point-to-point torus or mesh like thenetwork described above with reference to FIG. 4. A PAMI is configuredwith clients, each of which is in turn configured with certaincollections of such data communications resources—so that, for example,the PAMI client (302) in the PAMI (218) in the example of FIG. 7 canhave dedicated to its use a collection of data communications resourcescomposed of six segments (227) of shared memory, six Gigabit Ethernetadapters (238), and six Infiniband adapters (240). And the PAMI client(304) can have dedicated to its use six Fibre Channel adapters (242), aDMA controller (225), a torus network (108), and five segments (227) ofshared memory. And so on.

The DMA controllers (225, 226) in the example of FIG. 8A each isconfigured with DMA control logic in the form of a DMA engine (228,229), an injection FIFO buffer (230), and a receive FIFO buffer (232).The DMA engines (228, 229) can be implemented as hardware components,logic networks of a DMA controller, in firmware, as software operatingan embedded controller, as various combinations of software, firmware,or hardware, and so on. Each DMA engine (228, 229) operates on behalf ofendpoints to send and receive DMA transfer data through the network(108). The DMA engines (228, 229) operate the injection buffers (230,232) by processing first-in-first-out descriptors (234, 236) in thebuffers, hence the designation ‘injection FIFO’ and ‘receive FIFO.’

For further explanation, here is an example use case, a description ofthe overall operation of an example PUT DMA transfer using the DMAcontrollers (225, 226) and network (108) in the example of FIG. 8A: Anoriginating application (158), which is typically one instance of aparallel application running on a compute node, places a quantity oftransfer data (494) at a location in its RAM (155). The application(158) then calls a post function (480) on a context (512) of an originendpoint (352), posting a PUT instruction (390) into a work queue (282)of the context (512); the PUT instruction (390) specifies a targetendpoint (354) to which the transfer data is to be sent as well assource and destination memory locations. The application then calls anadvance function (482) on the context (512). The advance function (482)finds the new PUT instruction in its work queue (282) and inserts a datadescriptor (234) into the injection FIFO of the origin DMA controller(225); the data descriptor includes the source and destination memorylocations and the specification of the target endpoint. The origin DMAengine (225) then transfers the data descriptor (234) as well as thetransfer data (494) through the network (108) to the DMA controller(226) on the target side of the transaction. The target DMA engine(229), upon receiving the data descriptor and the transfer data, placesthe transfer data (494) into the RAM (156) of the target application atthe location specified in the data descriptor and inserts into thetarget DMA controller's receive FIFO (232) a data descriptor (236) thatspecifies the target endpoint and the location of the transfer data(494) in RAM (156). The target application (159) or application instancecalls an advance function (483) on a context (513) of the targetendpoint (354). The advance function (483) checks the communicationsresources assigned to its context (513) for incoming messages, includingchecking the receive FIFO (232) of the target DMA controller (226) fordata descriptors that specify the target endpoint (354). The advancefunction (483) finds the data descriptor for the PUT transfer andadvises the target application (159) that its transfer data has arrived.A GET-type DMA transfer works in a similar manner, with somedifferences, including, of course, the fact that transfer data flows inthe opposite direction. Similarly, typical SEND transfers also operatesimilarly, some with rendezvous protocols, some with eager protocols,with data transmitted in packets over the a network through non-DMAnetwork adapters or through DMA controllers.

The example of FIG. 8A includes two DMA controllers (225, 226). DMAtransfers between endpoints on separate compute nodes use two DMAcontrollers, one on each compute node. Compute nodes can be implementedwith multiple DMA controllers so that many or even all DMA transferseven among endpoints on a same compute node can be carried out using twoDMA engines. In some embodiments at least, however, a compute node, likethe example compute node (152) of FIG. 2, has only one DMA engine, sothat that DMA engine can be use to conduct both sides of transfersbetween endpoints on that compute node. For further explanation of thisfact, FIG. 8B sets forth a functional block diagram of an example DMAcontroller (225) operatively coupled to a network (108)—in anarchitecture where this DMA controller (225) is the only DMA controlleron a compute node—and an origin endpoint (352) and its target endpoint(354) are both located on the same compute node (152). In the example ofFIG. 8B, a single DMA engine (228) operates with two threads ofexecution (502, 504) on behalf of endpoints (352, 354) on a same computenode to send and receive DMA transfer data through a segment (227) ofshared memory. A transmit thread (502) injects transfer data into thenetwork (108) as specified in data descriptors (234) in an injectionFIFO buffer (230), and a receive thread (502) receives transfer datafrom the network (108) as specified in data descriptors (236) in areceive FIFO buffer (232).

The overall operation of an example PUT DMA transfer with the DMAcontrollers (225) and the network (108) in the example of FIG. 8B is: Anoriginating application (158), that is actually one of multipleinstances (158, 159) of a parallel application running on a compute node(152) in separate threads of execution, places a quantity of transferdata (494) at a location in its RAM (155). The application (158) thencalls a post function (480) on a context (512) of an origin endpoint(352), posting a PUT instruction (390) into a work queue (282) of thecontext (512); the PUT instruction specifies a target endpoint (354) towhich the transfer data is to be sent as well as source and destinationmemory locations. The application (158) then calls an advance function(482) on the context (512). The advance function (482) finds the new PUTinstruction (390) in its work queue (282) and inserts a data descriptor(234) into the injection FIFO of the DMA controller (225); the datadescriptor includes the source and destination memory locations and thespecification of the target endpoint. The DMA engine (225) thentransfers by its transmit and receive threads (502, 504) through thenetwork (108) the data descriptor (234) as well as the transfer data(494). The DMA engine (228), upon receiving by its receive thread (504)the data descriptor and the transfer data, places the transfer data(494) into the RAM (156) of the target application and inserts into theDMA controller's receive FIFO (232) a data descriptor (236) thatspecifies the target endpoint and the location of the transfer data(494) in RAM (156). The target application (159) calls an advancefunction (483) on a context (513) of the target endpoint (354). Theadvance function (483) checks the communications resources assigned toits context for incoming messages, including checking the receive FIFO(232) of the DMA controller (225) for data descriptors that specify thetarget endpoint (354). The advance function (483) finds the datadescriptor for the PUT transfer and advises the target application (159)that its transfer data has arrived. Again, a GET-type DMA transfer worksin a similar manner, with some differences, including, of course, thefact that transfer data flows in the opposite direction. And typicalSEND transfers also operate similarly, some with rendezvous protocols,some with eager protocols, with data transmitted in packets over the anetwork through non-DMA network adapters or through DMA controllers.

By use of an architecture like that illustrated and described withreference to FIG. 8B, a parallel application or an application messagingmodule that is already programmed to use DMA transfers can gain thebenefit of the speed of DMA data transfers among endpoints on the samecompute node with no need to reprogram the applications or theapplication messaging modules to use the network in other modes. In thisway, an application or an application messaging module, alreadyprogrammed for DMA, can use the same DMA calls through a same API forDMA regardless whether subject endpoints are on the same compute node oron separate compute nodes.

For further explanation, FIG. 10 sets forth a functional block diagramof example endpoints useful in parallel computers that implementendpoint-based parallel data processing in a PAMI according toembodiments of the present invention. In the example of FIG. 10, a PAMI(218) is implemented with instances on two separate compute nodes (152,153) that include four endpoints (338, 340, 342, 344). These endpointsare opaque objects used to address an origin or destination in a processand are constructed from a (client, task, context) tuple. Non-DMA SENDand RECEIVE instructions as well as DMA instructions such as PUT and GETaddress a destination by use of an endpoint object or endpointidentifier.

Each endpoint (338, 340, 342, 344) in the example of FIG. 10 is composedof a client (302, 303, 304, 305), a task (332, 333, 334, 335), and acontext (290, 292, 310, 312). Using a client a component in thespecification of an endpoint disambiguates the task and contextidentifiers, as these identifiers may be the same for multiple clients.A task is used as a component in the specification of an endpoint toconstruct an endpoint to address a process accessible through a context.A context in the specification of an endpoint identifies, refers to, orrepresents the specific context associated with a the destination ortarget task—because the context identifies a specific threading point ona task. A context offset identifies which threading point is to processa particular communications operation. Endpoints enable “crosstalk”which is the act of issuing communication on a local context with aparticular context offset that is directed to a destination endpointwith no correspondence to a source context or source context offset.

For efficient utilization of storage in an environment where multipletasks of a client reside on the same physical compute node, anapplication may choose to write an endpoint table (288, 300 on FIG. 7)in a segment of shared memory (227, 346, 348). It is the responsibilityof the application to allocate such segments of shared memory andcoordinate the initialization and access of any data structures sharedbetween processes. This includes any endpoint objects which are createdby one process or instance of an application and read by anotherprocess.

Endpoints (342, 344) on compute node (153) serve respectively twoapplication instances (157, 159). The tasks (334, 336) in endpoints(342, 344) are different. The task (334) in endpoint (342) is identifiedby the task ID (249) of application (157), and the task (336) inendpoint (344) is identified by the task ID (251) of application (159).The clients (304, 305) in endpoints (342, 344) are different, separateclients. Client (304) in endpoint (342) associates data communicationsresources (e.g., 294, 296, 314 on FIG. 7) dedicated exclusively to theuse of application (157), while client (305) in endpoint (344)associates data communications resources dedicated exclusively to theuse of application (159). Contexts (310, 312) in endpoints (342, 344)are different, separate contexts. Context (310) in endpoint (342)operates on behalf of application (157) a subset of the datacommunications resources of client (304), and context (312) in endpoint(344) operates on behalf of application (159) a subset of the datacommunications resources of client (305).

Contrasted with the PAMIs (218) on compute node (153), the PAMI (218) oncompute node (152) serves only one instance of a parallel application(158) with two endpoints (338, 340). The tasks (332, 333) in endpoints(338, 340) are the same, because they both represent a same instance ofa same application (158); both tasks (332,333) therefore are identified,either with a same variable value, references to a same object, or thelike, by the task ID (250) of application (158). The clients (302, 303)in endpoints (338, 340) are optionally either different, separateclients or the same client. If they are different, each associates aseparate collection of data communications resources. If they are thesame, then each client (302, 303) in the PAMI (218) on compute node(152) associates a same set of data communications resources and isidentified with a same value, object reference, or the like. Contexts(290, 292) in endpoints (338, 340) are different, separate contexts.Context (290) in endpoint (338) operates on behalf of application (158)a subset of the data communications resources of client (302) regardlesswhether clients (302, 303) are the same client or different clients, andcontext (292) in endpoint (340) operates on behalf of application (158)a subset of the data communications resources of client (303) regardlesswhether clients (302, 303) are the same client or different clients.Thus the tasks (332, 333) are the same; the clients (302, 303) can bethe same; and the endpoints (338, 340) are distinguished at least bydifferent contexts (290, 292), each of which operates on behalf of oneof the threads (251-254) of application (158), identified typically by acontext offset or a threading point.

Endpoints (338, 340) being as they are on the same compute node (152)can effect DMA data transfers between endpoints (338, 340) through DMAcontroller (225) and a segment of shared local memory (227). In theabsence of such shared memory (227), endpoints (338, 340) can effect DMAdata transfers through the DMA controller (225) and the network (108),even though both endpoints (338, 340) are on the same compute node(152). DMA transfers between endpoint (340) on compute node (152) andendpoint (344) on another compute node (153) go through DMA controllers(225, 226) and either a network (108) or a segment of shared remotememory (346). DMA transfers between endpoint (338) on compute node (152)and endpoint (342) on another compute node (153) also go through DMAcontrollers (225, 226) and either a network (108) or a segment of sharedremote memory (346). The segment of shared remote memory (346) is acomponent of a Non-Uniform Memory Access (‘NUMA’) architecture, asegment in a memory module installed anywhere in the architecture of aparallel computer except on a local compute node. The segment of sharedremote memory (346) is ‘remote’ in the sense that it is not installed ona local compute node. A local compute node is ‘local’ to the endpointslocated on that particular compute node. The segment of shared remotememory (346), therefore, is ‘remote’ with respect to endpoints (338,340) on compute node (158) if it is in a memory module on compute node(153) or anywhere else in the same parallel computer except on computenode (158).

Endpoints (342, 344) being as they are on the same compute node (153)can effect DMA data transfers between endpoints (342, 344) through DMAcontroller (226) and a segment of shared local memory (348). In theabsence of such shared memory (348), endpoints (342, 344) can effect DMAdata transfers through the DMA controller (226) and the network (108),even though both endpoints (342, 344) are on the same compute node(153). DMA transfers between endpoint (344) on compute node (153) andendpoint (340) on another compute node (152) go through DMA controllers(226, 225) and either a network (108) or a segment of shared remotememory (346). DMA transfers between endpoint (342) on compute node (153)and endpoint (338) on another compute node (158) go through DMAcontrollers (226, 225) and either a network (108) or a segment of sharedremote memory (346). Again, the segment of shared remote memory (346) is‘remote’ with respect to endpoints (342, 344) on compute node (153) ifit is in a memory module on compute node (158) or anywhere else in thesame parallel computer except on compute node (153).

For further explanation, FIG. 11 sets forth a flow chart illustrating anexample method of endpoint-based parallel data processing in a PAMI of aparallel computer according to embodiments of the present invention.FIG. 12 sets forth a data flow diagram that illustrates data flowseffected according to the method of FIG. 11. The method of FIG. 11 isdescribed below in this specification, therefore, with reference both toFIG. 11 and also to FIG. 12, using reference numbers from both FIGS. 11and 12.

The method of FIG. 11 is implemented in a PAMI (218) of a parallelcomputer composed of a number of compute nodes (102 on FIG. 1) thatexecute a parallel application, like those described above in thisspecification with reference to FIGS. 1-10. The PAMI (218) includes datacommunications endpoints (378 and, e.g., 338, 340, 342, 344 on FIG. 10),with each endpoint specifying data communications parameters for athread (e.g., 251, 252, 253, 254 on FIG. 10) of execution on a computenode, including specifications of a client (e.g., 302, 303, 304, 305 onFIG. 7), a context (290, 292, 310, 312 on FIG. 10), and a task (e.g.,332, 33, 334, 336 on FIG. 10), all as described above in thisspecification with reference to FIGS. 1-10. The endpoints are coupledfor data communications through the PAMI (218) and through datacommunications resources (e.g., 238, 240, 242, 246, 106, 108, 227 onFIG. 8A). The endpoints can be located on the same compute node or ondifferent compute nodes.

The method of FIG. 11 includes establishing (356) by anapplication-level entity (158), for collective operations of the PAMI, adata communications geometry (404). The geometry specifies, for tasksrepresenting processes of execution of the parallel application, a setof endpoints that are used in collective operations of the PAMI,including a plurality of endpoints for one of the tasks. Of course theseare PAMI tasks, where each task represents a canonical process ofexecution of the parallel application, and data communications insupport of collective operations are from process to processtraditionally with one endpoint per task—although ultimately, as will beseen, the addition of additional endpoints for one of the tasks willrepresent a strong additional parallelism. Incidentally, the phrasing of‘plurality of endpoint for one of the tasks,’ is only for convenience ofexplanation. The phrase actually means ‘plurality of endpoints for oneor more of the tasks.’ The need for this easing of the difficulty ofexplanation is clear when one considers that in an IBM BlueGene/Q™supercomputer, for example, with a million tasks organized in arectangular mesh with a thousand tasks on a side, one way to perform acollective BROADCAST is for the root task to incorporate into itsgeometry a thousand new endpoints, each of which is assignedresponsibility for transmitting transfer data down one of the onethousand rows of the mesh. One can quickly see why this example at leastis described in terms of thirty tasks and three rows instead of millionsand thousands. The principles illustrated are the same.

As mentioned, the geometry (404) specifies, for tasks representingprocesses of execution of the parallel application, a set of endpoints(405) that are used in collective operations of the PAMI, including aplurality of endpoints for one of the tasks. Each endpoint (405)specifies communications parameters for a combination of a task (406), aclient (408), and a context (410), a fact further explained withreference to Table 1.

TABLE 1 A Geometry For A PAMI Endpoint Task Client Context ep₀ t₀ cl₀co₀ ep₁ t₀ cl₀ co₁ ep₂ t₀ cl₀ co₂ ep₃ t₁ cl₀ co₃ ep₄ t₂ cl₀ co₄ . . . .. . . . . . . . ep_(N−3) t_(M−3) cl₀ co_(N−3) ep_(N−2) t_(M−2) cl₀co_(N−2) ep_(N−1) t_(M−1) cl₀ co_(N−1)

Each row in Table 1 represents an endpoint of a PAMI, with each of Nendpoints identified by an endpoint identifier ep₀ . . . ep_(N-1).Similarly, M tasks are identified as t₀ . . . t_(M-1). In this example,N is greater than M1; there are more endpoints than tasks because thefirst three endpoints, ep₀ . . . ep₂, are a plurality of endpoints foronly one task, task t₀. Possibly N can be quite a bit larger than M,because there is no limitation of the pluralization of endpoints to onlyone task. Tasks other than t₀ also can have more than one endpointproviding data communications on behalf of each such task. In thisexample, the endpoint are composed with only two clients, cl₀ and cl₁,two collections of data communication resources dedicated to the use ofthe application. Each endpoint includes a separate context, co₀ . . .co_(N-1), the number of contexts N being the same as the number ofendpoints in the geometry illustrated by Table 1, with each context co₀. . . co_(N-1) composed of a separate subset of one of the twocollections of data processing resources of the clients cl₀ and cl₀.

An application-level entity is an application, an instance of a parallelapplication, or an application messaging module, sometimes referred toin this discussion simply as an application. To establish (356) ageometry (404), an application-level entity, either at PAMIinitialization, or later, dynamically, at run time, can call a PAMIinitialization function (316 on FIG. 7) configured to establish ageometry by reading the geometry (404 on FIG. 7) from a PAMIconfiguration (318 on FIG. 7) or by using such configuration informationas a prototype of a geometry to be configured at run time. Theapplication can include in the geometry the endpoints defined for it inthe PAMI configuration (318 on FIG. 7), or the application candynamically add additional endpoints to augment parallel operations in amanner that will in fact be transparent to the application at run time.Clearly if an application messaging module initializes the PAMI, thenthe geometry of endpoints will always be transparent to the actualapplication, but the application itself will not administer endpoints atrun time even if it were the actual application itself that initializedthe PAMI and the geometry.

In the method of FIG. 1, establishing (356) a data communicationsgeometry (404) includes associating (358) with the geometry a list ofcollective algorithms (360) valid for use with the endpoints of thegeometry. The list (360) is ‘for use with the endpoints of the geometry’in the sense that, when an advance function finds a collectiveinstruction in its work queue, the advance function makes adetermination which collective algorithm to apply by consulting the list(360). Constructing such a list (360) of algorithms can be carried outat PAMI initialization by, for example, a call by an application-levelentity to a PAMI initialization function (316 on FIG. 7) that eitherreads the list from a PAMI configuration (318 on FIG. 7) or constructsthe list from a prototype list (360 on FIG. 7). The collectivealgorithms (360) are presented here in a structure (360) that uses ageometry identifier value (398) as a foreign key to associate thealgorithm list (360) and the geometry (404). The algorithm list (360) inthe example of FIG. 11, for ease of illustration, includes only thecollective algorithms for a BROADCAST (400) and a SCATTER (402), butreaders will recognize that such lists in actual embodiments also caninclude algorithms for other additional types of collective operationsalso, GATHERs, REDUCEs, ALLGATHERs, ALLREDUCEs, including manyvariations according to particular collective operations, computerarchitectures, availability of tree networks or mesh networks, whethershared memory segments are available, whether data buffers are disposedupon memory boundaries, whether the number of endpoints in a geometry iseven, odd, or a power of two, and so on. Because collective operationsinvolve data communications, configuration of a list of collectivealgorithms for a geometry also depends upon the nature of underlyingdata communications available to carry out any particular collectiveoperation. Available data communications types include, but are notlimited to, the following examples:

-   -   rendezvous network-based SEND types of data communications in        which both origin or root endpoints and target endpoints        communicate and participate in a data transfer, good for longer        messages, typically composed of handshakes transferring header        information followed by packet switched messaging or DMA        operations to transfer payload data,    -   eager network-based SEND types of data communications in which        only the origin or root endpoint conducts a data transfer,        merely informing the target endpoint that the transfer has        occurred, but requiring no communications or other participation        from the target endpoint,    -   rendezvous SEND types of data communications with operations        conducted, not through a network, but through shared memory, in        which both the origin or root endpoints and target endpoints        communicate and participate in a data transfer,    -   eager SEND types of data communications conducted, not through a        network, but through shared memory, in which only the origin or        root endpoint conducts a data transfer, merely informing target        endpoints that the transfer has occurred, but requiring no        communications or other participation from the target endpoints,    -   network-based DMA PUT types of data communications, useful for        fast transfers of small messages, sometimes containing header        data and payload data in a single transfer or packet—DMA        algorithms also can be used as components of other algorithms—as        for example a SEND algorithm that does an origin-target        handshake and then conducts payload transfers with PUTs,    -   DMA PUT types of data communications with transfers through        shared memory, again useful for fast transfers of small        messages, sometimes containing header data and payload data in a        single transfer or packet—DMA algorithms also can be used as        components of other algorithms—as for example a SEND algorithm        that does an origin-target handshake through a segment of shared        memory and then conducts payload transfers with PUTs,    -   data communications instruction types based on DMA GET        operations, either networked or through shared memory, and    -   data communications instruction types that include eager or        rendezvous RECEIVE operations, either with send-side matching of        SENDs or with receive-side matching.

The method of FIG. 11 also includes receiving (362) in one or moreendpoints of the geometry an instruction (390) for a collectiveoperation. It is said that the collective instruction (390) is receivedin ‘one or more endpoints’ because of the active nature of messaging insupport of collective operations in the PAMI. In an embodiment, in atraditional way, all instances of a parallel application call acollective operation at about the same time and endpoints of thegeometry all receive a corresponding collective instruction at the sametime, some parameterized for a root, other parameterized for non-roots.In other embodiments, all endpoints are configured with dispatchcallback function for messaging in support of collective operations, andonly endpoints operating on behalf of a root task actually receive andexecute a corresponding collective instruction, possible because all theendpoints will carry out the proper data processing through callbacksupon receiving messaging from the root tasks through one or moreendpoints serving the root tasks—as in a BROADCAST or SCATTER, forexample. In other embodiments, one or more endpoints serving a roottasks are configured with a dispatch callback function for messaging insupport of collective operations, and only endpoints operating on behalfof non-root tasks actually receive and execute a correspondingcollective instruction, possible because the endpoints serving the roottasks will carry out the proper data processing through callbacks uponreceiving messaging from the non-root tasks through one or moreendpoints serving the non-root tasks—as in a GATHER or REDUCE, forexample.

In this example, the received instruction (382) is characterized by aninstruction type (382) that specifies communications of transfer dataamong the endpoints (378) of the geometry. Types of such a collectiveinstruction include, but are not limited to, the following exampleinstruction types:

-   -   a BROADCAST instruction requiring transfer of the entire        contents of a send buffer from an endpoint of a root task to        endpoints of all the tasks in the geometry,    -   a SCATTER instruction requiring transfer of a corresponding        segments of a send buffer from an endpoint of a root task to        endpoints of all the tasks in the geometry,    -   a GATHER instruction requiring transfer of the entire contents        of send buffers from endpoints of all the tasks in the geometry        to corresponding segments of a receive buffer of endpoints of a        root task,    -   an ALLGATHER instruction requiring transfer of the entire        contents of send buffers from endpoints of all the tasks in the        geometry to corresponding segments of a receive buffer of        endpoints of a root task and a transfer of the resulting gather        back to endpoints of all the tasks in the geometry,    -   a REDUCE instruction requiring transfer of, as well as a logical        or mathematical operation upon, the entire contents of send        buffers from endpoints of all the tasks in the geometry to        corresponding segments of a receive buffer of endpoints of a        root task, and    -   an ALLREDUCE instruction requiring transfer of, as well as a        logical or mathematical operation upon, the entire contents of        send buffers from endpoints of all the tasks in the geometry to        corresponding segments of a receive buffer of endpoints of a        root task and a transfer of the resulting reduction back to        endpoints of all the tasks in the geometry.

In the example of FIG. 11, an originating application (158), typicallyan instance of a parallel application running on a compute node, placesa quantity of transfer data at a location in its RAM, and includes thememory location and the quantity of transfer data in parameters (383) ofthe instruction (390) for a collective operation. The instruction (390)then is received in an endpoint of a root tasks (352) through operationof a post function (480) called by the originating application (158) ona context of the endpoint (352), posting the instruction (390) to a workqueue of a context of the endpoint (352). The instruction parameters(383) also typically specify one or more callback functions, a dispatchcallback, a done callback, or the like, to support and implement datacommunications in support of the instruction (390). Any done callback isregistered in the PAMI (218) for later use, upon completion of theoverall execution of the collective instruction (390). Such a donecallback function is an application-level instruction called by anadvance function of the PAMI when execution of the data communicationsinstruction is fully complete. Such a done callback can carry out anyactions desired by the application at that point, but readers willrecognize that a typical purpose of the done callback is to advise thecalling application (158) (or a corresponding application messagingfunction) of completion of the data transfer pursuant to the collectiveinstruction. The application's post (480) of the instruction (390) isnon-blocking, so that the application continues other work while thePAMI executes the collective instruction (390). Not blocking to wait forthe data communications instruction to complete, it is common for theapplication to want a callback to advise of completion of data transferspursuant to the collective instruction.

The method of FIG. 11 also includes executing (365) the collectiveinstruction through the endpoints (378) in dependence upon the geometry(404). Executing a collective instruction involves data communicationsamong the endpoints, and in the example method of FIG. 11, executing(364) the collective instruction includes dividing (370) such datacommunications operations among a plurality (376) of endpoints for oneof the tasks. In this example, endpoints 0, 1, and 2 are taken as threeendpoints of a single root tasks for a collective operation such as aBROADCAST. In the method of FIG. 11, dividing (379) data communicationsoperations includes assigning (372) to each of the plurality (376) ofendpoints for one of the tasks (a single root task, in this example) aseparate subset of the endpoints in the geometry. Here, endpoint 0 isassigned subset (392), endpoint 1 is assigned subset (394), and endpoint2 is assigned subset (396).

Also in the method of FIG. 11, dividing (370) data communicationsoperations includes transferring (374), by each of the plurality (376)of endpoints for one of the tasks, the transfer data (384) to each ofthe endpoints in the subset assigned to that endpoint. That is, allendpoints in the geometry receive all of the transfer data, includingeach of the plurality (376) that carries out the actual transfer. Thuseach of the endpoints in the plurality of endpoints of the root tasksreceives a transfer of all the transfer data, redundancy because thesethree endpoints 0, 1, and 2 are all directed at a single task andtherefore at a single receive buffer (416). The term ‘geometry’ ischosen to represent a collection of endpoints and tasks because itimplies a shape for data communications. In FIG. 12 it is seen thatamong a collection of receive buffers (414), there is a receive bufferfor each task in the geometry. In the example of FIG. 12, it is takenthat each task has a single target receive buffer, including the taskserved by endpoints 0, 1, and 2. Because all the transfer data is sentto all endpoints of the geometry, endpoints 0, 1, and 2 each receive allthe transfer data, but two of these receptions are redundant andtherefore discarded, placing only one of the receptions of transfer datainto the receive buffer (416) served by endpoints 0, 1, and 2.

Also in the example of FIGS. 11 and 12, the transfers (374) of data arecarried out by the three plural endpoints 0, 1, and 2 in paralleloperations. Thus, instead of N transfers required to be carried out by asingle advance function of a single context of a single endpoint of aroot task, now the transfers are divided by three among the threeendpoints 0, 1, and 2 a proceed in parallel on three separate sets ofdata communications resources of three separate contexts from threeseparate endpoints, increasing throughput of the pertinent datacommunications by a factor of three. Readers will recognize that thebenefit of such parallelism is not limited to the factor of threeillustrated here, but is limited only by the quantity of datacommunications resources available for assignment to contexts in theendpoints of the geometry. In addition, there is no limitation of such aplurality of endpoints for a single task that achieve such parallelismto the data communications resources of a single compute node. On thecontrary, an application or application messaging module that implementssuch a geometry of endpoints and tasks that includes a plurality ofendpoints for one of the tasks in the geometry can instantiate endpointsof the geometry on compute nodes other than the compute node upon whichthe task having the plurality of endpoints is installed. In this way,availability of data communications across compute nodes is still alimiting factor, but the limit is not defined by the data communicationsresources on any single compute node. There is a present trend amongsupercomputer installations for torus or mesh networks to be implementedin five dimension, so that it is expected that each compute node couldsupport five endpoints transmitting and five endpoints receiving data atthe same time. An application or application messaging module operatingon such a supercomputer, however, can instantiate endpoints that arephysically located on neighboring compute nodes, however, so that anyparticular data transfer can gain the parallelism of five or ten orfifteen or more endpoints physically located across several, or evenmany, compute nodes.

For further explanation, FIG. 13 sets forth a flow chart illustrating afurther example method of endpoint-based parallel data processing in aPAMI of a parallel computer according to embodiments of the presentinvention. FIG. 14 sets forth a data flow diagram that illustrates dataflows effected according to the method of FIG. 13. The method of FIG. 13is described below in this specification, therefore, with reference bothto FIG. 13 and also to FIG. 14, using reference numbers from both FIGS.13 and 14. The method of FIG. 13, like the method of FIG. 11, isimplemented in a PAMI (218) of a parallel computer composed of a numberof compute nodes (102 on FIG. 1) that execute a parallel application,like those described above in this specification with reference to FIGS.1-10. The PAMI (218) includes data communications endpoints (378 and,e.g., 338, 340, 342, 344 on FIG. 10), with each endpoint specifying datacommunications parameters for a thread (e.g., 251, 252, 253, 254 on FIG.10) of execution on a compute node, including specifications of a client(e.g., 302, 303, 304, 305 on FIG. 7), a context (290, 292, 310, 312 onFIG. 10), and a task (e.g., 332, 33, 334, 336 on FIG. 10), all asdescribed above in this specification with reference to FIGS. 1-10. Theendpoints are coupled for data communications through the PAMI (218) andthrough data communications resources (e.g., 238, 240, 242, 246, 106,108, 227 on FIG. 8A). The endpoints can be located on the same computenode or on different compute nodes.

The method of FIG. 13 is also like the method of FIG. 11 including, asit does, establishing (356) by an application-level entity (158), forcollective operations of the PAMI, a data communications geometry (404),receiving (362) in one or more endpoints of the geometry an instruction(390) for a collective operation, and executing (365) the collectiveinstruction through the endpoints (378) in dependence upon the geometry(404), including dividing (370) data communications operations among aplurality (376) of endpoints for one of the tasks. In the method of FIG.13, however, unlike the method of FIG. 11, dividing (366) datacommunications operations among a plurality of endpoints for one of thetasks includes assigning (418) portions of the transfer data (384) toeach of the plurality (376) of endpoints for one of the tasks. That is,the send buffer (412) is segmented into individual portions, and thenumber of portions is equal to the number M of tasks of endpoints of thegeometry, with each portion 0 . . . M−1 of the transfer data (384)corresponding to one of 0 . . . M−1 tasks of the geometry—and thereforeto one of the 0 . . . M−1 receive buffers. Because at least one of thetasks has a plurality of endpoints (376), there are more endpoints thantasks in the geometry, so that N, the number of endpoints in thegeometry, is greater than M.

These portions of the transfer data are then assigned to the pluralityof endpoints for one of the tasks for transfer to the endpoints of thegeometry. In this example, there are P such endpoints 0 . . . P−1representing the plurality (376) of endpoints for one task, and theportions of the transfer data (384) are assigned to the plurality in Psegments 0 . . . P−1. So each of the P endpoints 0 . . . P−1 in theplurality (376) of endpoints for one task, the root task in thecollective operation, is assigned for transfer a corresponding subset, 0. . . P−1 respectively, of the transfer data (384) in the send buffer(412).

Also in the method of FIG. 13, dividing (366) data communicationsoperations includes transferring (420), in parallel operations by theplurality (376) of endpoints for one of the tasks to one endpoint foreach task of the geometry, a corresponding portion of the transfer data.Because the transfers are ‘to one endpoint for each task of thegeometry,’ only one of the plurality of endpoints for the root tasks, inthis example endpoint 0, receives a transmission of a segment of thetransfer data. The plurality (376) of endpoints effecting the transferhave access to the geometry, for example, information like that inTable 1. In such a geometry, at least one, but possibly many, of thetasks have more than one endpoint. Each of the plurality (376) ofendpoints effecting the transfer observes the geometry, and, for eachtask with more than one endpoint, selects one of those endpoints toreceive the segment of the transfer data corresponding to the task.

In this example, endpoint 0 selects itself to receive the transfer ofsegment 0 of the send buffer (412), although it could have selected anyone of the P endpoints in the plurality of endpoints of task t₀ toreceive segment 0 of the transfer data. In this example, all theremaining P−1 endpoints of the root task receive no transmission oftransfer data, therefore having no redundant transmissions and no needto discard any redundant packets. In this way, segment 0 of the transferdata is transferred to receive buffer 0 of task t₀ in the geometry,segment 1 of the transfer data is transferred to receive buffer 1 oftask t₁ in the geometry, segment 2 of the transfer data is transferredto receive buffer 2 of task t₂ in the geometry, and so on, until segmentM−3 of the transfer data is transferred to receive buffer M−3 of taskT_(m-3) in the geometry, segment M−2 of the transfer data is transferredto receive buffer M−2 of task T_(M-2) in the geometry, and segment M−1of the transfer data is transferred to receive buffer M−1 of taskT_(M-1) in the geometry.

Also in the example of FIGS. 13 and 14, the transfers (420) of data arecarried out by the P plural endpoints 0 . . . P−1 in paralleloperations. Thus, instead of M transfers required to be carried out by asingle advance function of a single context of a single endpoint of aroot task, now the transfers are divided by P among the P endpoints inthe plurality of endpoints of the root task and proceed in parallel on Pseparate sets of data communications resources of P separate contextsfrom P separate endpoints, increasing the overall throughput rate of thepertinent data communications by a factor of P. In a geometry with amillion tasks arranged in a square mesh with one thousand tasks in eachof a thousand rows, an application or application messaging module caninstantiate P=one thousand endpoints for the root task, each withseparate data communications resources running in parallel, and transferin a collective SCATTER operation, for example, a million segments ofits send buffer in one thousand parallel transmissions across onethousand endpoints of the root task. The benefit of such parallelism islimited only by the quantity of data communications resources availablefor assignment to contexts in the endpoints of the geometry, and thereis no limitation of such a plurality of endpoints for a single task tothe data communications resources of a single compute node.

For further explanation, FIG. 15 sets forth a flow chart illustrating afurther example method of endpoint-based parallel data processing in aPAMI of a parallel computer according to embodiments of the presentinvention. FIG. 16 sets forth a data flow diagram that illustrates dataflows effected according to the method of FIG. 15. The method of FIG. 15is described below in this specification, therefore, with reference bothto FIG. 15 and also to FIG. 16, using reference numbers from both FIGS.13 and 14. The method of FIG. 15, like the method of FIG. 11, isimplemented in a PAMI (218) of a parallel computer composed of a numberof compute nodes (102 on FIG. 1) that execute a parallel application,like those described above in this specification with reference to FIGS.1-10. The PAMI (218) includes data communications endpoints (378 and,e.g., 338, 340, 342, 344 on FIG. 10), with each endpoint specifying datacommunications parameters for a thread (e.g., 251, 252, 253, 254 on FIG.10) of execution on a compute node, including specifications of a client(e.g., 302, 303, 304, 305 on FIG. 7), a context (290, 292, 310, 312 onFIG. 10), and a task (e.g., 332, 33, 334, 336 on FIG. 10), all asdescribed above in this specification with reference to FIGS. 1-10. Theendpoints are coupled for data communications through the PAMI (218) andthrough data communications resources (e.g., 238, 240, 242, 246, 106,108, 227 on FIG. 8A). The endpoints can be located on the same computenode or on different compute nodes.

The method of FIG. 15 is also like the method of FIG. 11 including, asit does, establishing (356) by an application-level entity (158), forcollective operations of the PAMI, a data communications geometry (404),receiving (362) in one or more endpoints of the geometry an instruction(390) for a collective operation, and executing (365) the collectiveinstruction through the endpoints (378) in dependence upon the geometry(404), including dividing (370) data communications operations among aplurality (376) of endpoints for one of the tasks. In the method of FIG.15, however, unlike the method of FIG. 11, dividing (366) datacommunications operations among a plurality of endpoints for one of thetasks includes assigning (422) to each of the plurality (376) ofendpoints for one of the tasks (a single root task in this example) aseparate subset of the tasks in the geometry. Here, endpoint 0 isassigned subset (392), endpoint 1 is assigned subset (394), and endpoint2 is assigned subset (396). In the method of FIG. 11, it is theendpoints of the geometry that are divided among the plurality (376),not, as here, the tasks.

Also in the method of FIG. 15, dividing (366) data communicationsoperations among a plurality of endpoints for one of the tasks alsoincludes transferring (424), by one endpoint of each task in thegeometry, a different portion of the transfer data to one of theplurality of endpoints for one of the tasks. In this example there are Msend buffers for M tasks, t₀ . . . t_(M-1). The tasks as such are notshown in FIG. 16, but there is one send buffer (430) per task of thegeometry (378). The geometry include a plurality (376) of endpoints 0,1, 2 for one task, t₀, the root task, so that there are more endpointsin the geometry than there are tasks. The transfer data is the separatedata segments in each of the send buffer (430), so that only oneendpoint per send buffer, that is, only one endpoint per tasks, need toeffect a data transfer to one of the plurality (376) of endpoints of theroot task. Because endpoints 0, 1, 2 are endpoints of the same task t₀,each of them can serve send buffer 0 (416), although only one needs todo so for this data transfer. In this example, advance functions incontexts of each endpoint check the geometry upon receiving thecollective instruction in their work queues and, if they find more thanone endpoint for their task, agree that the lowest ranking endpoint isto transfer the data, while the other endpoints of the task do nothingfurther. In this example, advance functions of endpoints 0, 1, and 2note from the geometry that each of them is an endpoint of the same taskt₀ and agree that only endpoint 0 will effect the transfer of data fromtheir send buffer 0 (416).

The advance functions also note from the geometry the number ofendpoints in the plurality (376) of endpoints of the root task and inferfrom that which of the plurality (376) is to receive their transferdata. Each task is identified by a sequential integer 0 . . . M−1, and,in this example, each advance function notes that there are three tasks0, 1, 2 in the plurality (376), divides the M number of send buffers ortasks into three subranges, observes in which of the three subrangesfalls its task ID, and sends the contents of its send buffer to thecorresponding one of the plurality (376) of endpoints of the root task.Endpoint 0 for task t₀ finds that its task ID falls in the first thirdof the range 0 . . . M−1 and therefore sends the contents of its sendbuffer to endpoint 0, which in this example happens to be itself.Endpoint M/3+1 finds that its task ID falls in the second third of therange 0 . . . M−1 and therefore sends the contents of its send buffer toendpoint 1. And so on.

Also in the method of FIG. 15, dividing (366) data communicationsoperations among the plurality (376) of endpoints for one of the tasks,here the root task, includes gathering (426), in parallel operations bythe plurality of endpoints for one task, the root task, all of thetransfer data into a single receive buffer (428). The receive buffer(428) is divided into segments corresponding to portions of the transferdata transferred from endpoints in the geometry. Here, with each of thethree endpoints 0, 1, 2 in the plurality (376) of endpoints of the roottask assigned a separate subset of the tasks in the geometry(respectively 392, 394, 396), the M tasks and M send buffers areeffectively divided into three subranges, 0 . . . M/3, (M/3)+1 . . .2(M/3), and (2(M/3))+1 . . . M−1. Endpoint 0 receives the data transfersfrom send buffers of tasks in the first third (392) and places each in acorresponding segment of the first third (438) of the receive buffer(428) according to task ID: the transfer data for task t₀ from sendbuffer 0 is placed in segment 0 of the receive buffer (428), thetransfer data for task t₁ from send buffer 1 is placed in segment 1 ofthe receive buffer (428), and so on. Similarly endpoint 1 receives thedata transfers from send buffers of tasks in the second third (394) ofthe tasks and send buffers (430) and places each in a correspondingsegment of the second third (440) of the receive buffer (428) accordingto task ID. And endpoint 2 receives the data transfers from send buffersof tasks in the third (396) of the tasks and send buffers (430) andplaces each in a corresponding segment of the third (442) of the receivebuffer (428) according to task ID, ending with segments M−3, M−2, andM−1 corresponding to the contents of send buffers for tasks t_(M-3),t_(M-2), and t_(M-1).

The method of FIG. 15 effects a data flow in a direction opposite tothat of the methods of FIGS. 11 and 13, but the benefits of additionalparallelism are the same. In the methods of FIGS. 11 and 13, the dataflow is from a send buffer of a root task through endpoints of the roottask to endpoints of all tasks in the geometry. In the method of FIG.15, the data flow is endpoints of all tasks in the geometry throughendpoints of a root task into a receive buffer of the root task.

In the example of FIGS. 15 and 16, the gathering of the data is carriedout by the three plural endpoints 0, 1, 2 in parallel operations. Thus,instead of M transfers required to be gathered into the receive bufferby a single advance function of a single context of a single endpoint ofthe root task, now the transfers are divided, received, and gatheredamong the three endpoints of the root task and proceed in parallel onthree separate sets of data communications resources of three separatecontexts across three separate endpoints, increasing the overallthroughput rate of the pertinent data communications by a factor ofthree. Of course ‘three’ is an example, not a limitation of theinvention, embodiments of which can use any number of endpoints in aplurality for a task. In a geometry with a million tasks arranged in asquare mesh with one thousand tasks in each of a thousand rows, anapplication or application messaging module can instantiate one thousandendpoints for the root task, each with separate data communicationsresources running in parallel, assign to each a row containing athousand tasks with a thousand send buffers and gather into a receivebuffer of a root task in a collective GATHER operation, for example, thecontents of a million send buffers through one thousand parallelreceptions across one thousand endpoints of the root task—athousand-fold gain in throughput compared with gathering the transferdata through a single link of the root task. Again readers should notethat the benefit of such parallelism is limited only by the quantity ofdata communications resources available for assignment to contexts inthe endpoints of the geometry, and there is no limitation of such aplurality of endpoints for a single task to the data communicationsresources of a single compute node.

Retransmitting such gathered data to endpoints of all tasks canimplement a PAMI-level collective ALLGATHER operation. Adding amathematical or logical operation on the gathered data can implement aPAMI-level collective REDUCE operation. Retransmitting the contents ofthe receive buffer to endpoints of all tasks after such a REDUCEoperation can implement a PAMI-level collective ALLREDUCE operation. Andso on. All these collective operations can gain significant speed fromthe massive parallelism that is possible with endpoint-based paralleldata processing in a PAMI of a parallel computer according toembodiments of the present invention. Moreover, all these advantages,which do come at some cost in terms of complexity, are effected in amanner that can be entirely invisible to a calling application, whichhas simply issued a collective instruction to an application messagingmodule such as an MPI. As far as the application is concerned, it willeffect a single BROADCAST to all ranks of an MPI communicator, and allthe additional parallelism of embodiments of the present invention willbe brought to bear on behalf of the application with no awareness of iton the part of the application. The application will only know that itsBROADCAST went much faster that it would go on prior art messagingmiddleware without the benefits of endpoint-based parallel dataprocessing in a PAMI according to embodiments of the present invention.

Example embodiments of the present invention are described largely inthe context of a fully functional parallel computer that implementsendpoint-based parallel data processing in a PAMI. Readers of skill inthe art will recognize, however, that the present invention also may beembodied in a computer program product disposed upon computer readablestorage media for use with any suitable data processing system. Suchcomputer readable storage media may be any storage medium formachine-readable information, including magnetic media, optical media,or other suitable media. Examples of such media include magnetic disksin hard drives or diskettes, compact disks for optical drives, magnetictape, and others as will occur to those of skill in the art. Personsskilled in the art will immediately recognize that any computer systemhaving suitable programming means will be capable of executing the stepsof the method of the invention as embodied in a computer programproduct. Persons skilled in the art will recognize also that, althoughsome of the example embodiments described in this specification areoriented to software installed and executing on computer hardware,nevertheless, alternative embodiments implemented as firmware or ashardware are well within the scope of the present invention.

As will be appreciated by those of skill in the art, aspects of thepresent invention may be embodied as method, apparatus or system, orcomputer program product. Accordingly, aspects of the present inventionmay take the form of an entirely hardware embodiment or an embodimentcombining software and hardware aspects (firmware, resident software,micro-code, microcontroller-embedded code, and the like) that may allgenerally be referred to herein as a “circuit,” “module,” “system,” or“apparatus.” Furthermore, aspects of the present invention may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

Any combination of one or more computer readable media may be utilized.Such a computer readable medium may be a computer readable signal mediumor a computer readable storage medium. A computer readable storagemedium may be, for example, but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,or device, or any suitable combination of the foregoing. More specificexamples (a non-exhaustive list) of the computer readable storage mediumwould include the following: an electrical connection having one or morewires, a portable computer diskette, a hard disk, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, a portable compactdisc read-only memory (CD-ROM), an optical storage device, a magneticstorage device, or any suitable combination of the foregoing. In thecontext of this document, a computer readable storage medium may be anytangible medium that can contain, or store a program for use by or inconnection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer readable medium may be transmitted using anyappropriate medium, including but not limited to wireless, wireline,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described in this specificationwith reference to flowchart illustrations and/or block diagrams ofmethods, apparatus (systems) and computer program products according toembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof computer apparatus, methods, and computer program products accordingto various embodiments of the present invention. In this regard, eachblock in a flowchart or block diagram may represent a module, segment,or portion of code, which comprises one or more executable instructionsfor implementing the specified logical function(s). It should also benoted that, in some alternative implementations, the functions noted inthe block may occur out of the order noted in the figures. For example,two blocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustrations,and combinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

1. A method of endpoint-based parallel data processing in a parallelactive messaging interface (‘PAMI’) of a parallel computer, the parallelcomputer comprising a plurality of compute nodes that execute a parallelapplication, the PAMI comprising data communications endpoints, eachendpoint comprising a specification of data communications parametersfor a thread of execution on a compute node, including specifications ofa client, a context, and a task, the endpoints coupled for datacommunications through the PAMI, the method comprising: establishing byan application-level entity, for collective operations of the PAMI, adata communications geometry, the geometry specifying, for tasksrepresenting processes of execution of the parallel application, a setof endpoints that are used in collective operations of the PAMI,including a plurality of endpoints for one of the tasks; receiving inone or more endpoints of the geometry an instruction for a collectiveoperation, the instruction specifying communications of transfer dataamong the endpoints of the geometry; and executing the instruction for acollective operation through the endpoints in dependence upon thegeometry, including dividing data communications operations among theplurality of endpoints for one of the tasks.
 2. The method of claim 1wherein establishing a data communications geometry further comprisesassociating with the geometry a list of collective algorithms valid foruse with the endpoints of the geometry.
 3. The method of claim 1 whereindividing data communications operations further comprises: assigning toeach of the plurality of endpoints for one of the tasks a separatesubset of the endpoints in the geometry; and transferring, in paralleloperations by each of the plurality of endpoints for one of the tasks,the transfer data to each of the endpoints in the subset assigned tothat endpoint.
 4. The method of claim 1 wherein dividing datacommunications operations further comprises: assigning portions of thetransfer data to each of the plurality of endpoints for one of thetasks, the number of portions being equal to the number of tasks ofendpoints of the geometry, each portion of the transfer datacorresponding to a task of the geometry; and transferring, in paralleloperations by the plurality of endpoints for one of the tasks to oneendpoint for each task of the geometry, a corresponding portion of thetransfer data.
 5. The method of claim 1 wherein dividing datacommunications operations further comprises: assigning to each of theplurality of endpoints for one of the tasks a separate subset of thetasks in the geometry; transferring, by one endpoint of each task in thegeometry, a different portion of the transfer data to one of theplurality of endpoints for one of the tasks; and gathering, in paralleloperations by the plurality of endpoints for one task, all of thetransfer data into a single receive buffer, the receive buffer dividedinto segments each of which corresponds to a portion of the transferdata transferred from an endpoint of a task in the geometry.
 6. Themethod of claim 1 wherein: each client comprises a collection of datacommunications resources dedicated to the exclusive use of anapplication-level data processing entity; each context comprises asubset of the collection of data processing resources of a client,context functions, and a work queue of data transfer instructions to beperformed by use of the subset through the context functions operated byan assigned thread of execution; and each task represents a process ofexecution of the parallel application.
 7. The method of claim 1 whereineach context carries out, through post and advance operations, datacommunications for the application on data communications resources inthe exclusive possession of that context.
 8. The method of claim 1wherein each context carries out data communications operationsindependently and in parallel with other contexts.
 9. A parallelcomputer that implements endpoint-based parallel data processing in aparallel active messaging interface (‘PAMI’) of the parallel computer,the parallel computer comprising a plurality of compute nodes thatexecute a parallel application, the PAMI comprising data communicationsendpoints, each endpoint comprising a specification of datacommunications parameters for a thread of execution on a compute node,including specifications of a client, a context, and a task, theendpoints coupled for data communications through the PAMI, the computenodes comprising computer processors operatively coupled to computermemory having disposed within it computer program instructions that,when executed by the computer processors, cause the parallel computer tofunction by: establishing by an application-level entity, for collectiveoperations of the PAMI, a data communications geometry, the geometryspecifying, for tasks representing processes of execution of theparallel application, a set of endpoints that are used in collectiveoperations of the PAMI, including a plurality of endpoints for one ofthe tasks; receiving in one or more endpoints of the geometry aninstruction for a collective operation, the instruction specifyingcommunications of transfer data among the endpoints of the geometry; andexecuting the instruction for a collective operation through theendpoints in dependence upon the geometry, including dividing datacommunications operations among the plurality of endpoints for one ofthe tasks.
 10. The parallel computer of claim 9 wherein establishing adata communications geometry further comprises associating with thegeometry a list of collective algorithms valid for use with theendpoints of the geometry.
 11. The parallel computer of claim 9 whereindividing data communications operations further comprises: assigning toeach of the plurality of endpoints for one of the tasks a separatesubset of the endpoints in the geometry; and transferring, in paralleloperations by each of the plurality of endpoints for one of the tasks,the transfer data to each of the endpoints in the subset assigned tothat endpoint.
 12. The parallel computer of claim 9 wherein dividingdata communications operations further comprises: assigning portions ofthe transfer data to each of the plurality of endpoints for one of thetasks, the number of portions being equal to the number of tasks ofendpoints of the geometry, each portion of the transfer datacorresponding to a task of the geometry; and transferring, in paralleloperations by the plurality of endpoints for one of the tasks to oneendpoint for each task of the geometry, a corresponding portion of thetransfer data.
 13. The parallel computer of claim 9 wherein dividingdata communications operations further comprises: assigning to each ofthe plurality of endpoints for one of the tasks a separate subset of thetasks in the geometry; transferring, by one endpoint of each task in thegeometry, a different portion of the transfer data to one of theplurality of endpoints for one of the tasks; and gathering, in paralleloperations by the plurality of endpoints for one task, all of thetransfer data into a single receive buffer, the receive buffer dividedinto segments each of which corresponds to a portion of the transferdata transferred from an endpoint of a task in the geometry.
 14. Theparallel computer of claim 9 wherein: each client comprises a collectionof data communications resources dedicated to the exclusive use of anapplication-level data processing entity; each context comprises asubset of the collection of data processing resources of a client,context functions, and a work queue of data transfer instructions to beperformed by use of the subset through the context functions operated byan assigned thread of execution; and each task represents a process ofexecution of the parallel application.
 15. A computer program productfor endpoint-based parallel data processing in a parallel activemessaging interface (‘PAMI’) of a parallel computer, the parallelcomputer comprising a plurality of compute nodes that execute a parallelapplication, the PAMI comprising data communications endpoints, eachendpoint comprising a specification of data communications parametersfor a thread of execution on a compute node, including specifications ofa client, a context, and a task, the endpoints coupled for datacommunications through the PAMI, the computer program product disposedupon a computer readable storage medium, the computer program productcomprising computer program instructions that, when installed andexecuted, cause the parallel computer to function by: establishing by anapplication-level entity, for collective operations of the PAMI, a datacommunications geometry, the geometry specifying, for tasks representingprocesses of execution of the parallel application, a set of endpointsthat are used in collective operations of the PAMI, including aplurality of endpoints for one of the tasks; receiving in one or moreendpoints of the geometry an instruction for a collective operation, theinstruction specifying communications of transfer data among theendpoints of the geometry; and executing the instruction for acollective operation through the endpoints in dependence upon thegeometry, including dividing data communications operations among theplurality of endpoints for one of the tasks.
 16. The computer programproduct of claim 15 wherein establishing a data communications geometryfurther comprises associating with the geometry a list of collectivealgorithms valid for use with the endpoints of the geometry.
 17. Thecomputer program product of claim 15 wherein dividing datacommunications operations further comprises: assigning to each of theplurality of endpoints for one of the tasks a separate subset of theendpoints in the geometry; and transferring, in parallel operations byeach of the plurality of endpoints for one of the tasks, the transferdata to each of the endpoints in the subset assigned to that endpoint.18. The computer program product of claim 15 wherein dividing datacommunications operations further comprises: assigning portions of thetransfer data to each of the plurality of endpoints for one of thetasks, the number of portions being equal to the number of tasks ofendpoints of the geometry, each portion of the transfer datacorresponding to a task of the geometry; and transferring, in paralleloperations by the plurality of endpoints for one of the tasks to oneendpoint for each task of the geometry, a corresponding portion of thetransfer data.
 19. The computer program product of claim 15 whereindividing data communications operations further comprises: assigning toeach of the plurality of endpoints for one of the tasks a separatesubset of the tasks in the geometry; transferring, by one endpoint ofeach task in the geometry, a different portion of the transfer data toone of the plurality of endpoints for one of the tasks; and gathering,in parallel operations by the plurality of endpoints for one task, allof the transfer data into a single receive buffer, the receive bufferdivided into segments each of which corresponds to a portion of thetransfer data transferred from an endpoint of a task in the geometry.20. The computer program product of claim 15 wherein: each clientcomprises a collection of data communications resources dedicated to theexclusive use of an application-level data processing entity; eachcontext comprises a subset of the collection of data processingresources of a client, context functions, and a work queue of datatransfer instructions to be performed by use of the subset through thecontext functions operated by an assigned thread of execution; and eachtask represents a process of execution of the parallel application.